Metrics for assessing community assembly processes
source('../env.R')community_data = read_csv(filename(COMMUNITY_OUTPUT_DIR, 'community_assembly_metrics_using_relative_abundance.csv'))
head(community_data)colnames(community_data) [1] "mntd_standard" "mntd_normal" "mntd_actual" "mass_fdiv_standard"
[5] "mass_fdiv_normal" "mass_fdiv_actual" "beak_width_fdiv_standard" "beak_width_fdiv_normal"
[9] "beak_width_fdiv_actual" "hwi_fdiv_standard" "hwi_fdiv_normal" "hwi_fdiv_actual"
[13] "city_id" "urban_pool_size" min(community_data$mntd_standard)[1] -2.338219max(community_data$mntd_standard)[1] 2.34743min(community_data$beak_width_fdiv_standard)[1] -2.685152max(community_data$beak_width_fdiv_standard)[1] 1.931681min(community_data$hwi_fdiv_standard)[1] -2.254896max(community_data$hwi_fdiv_standard)[1] 2.315362min(community_data$mass_fdiv_standard)[1] -2.351968max(community_data$mass_fdiv_standard)[1] 2.114779min(community_data$mntd_normal)[1] 0max(community_data$mntd_normal)[1] 1min(community_data$beak_width_fdiv_normal)[1] 0max(community_data$beak_width_fdiv_normal)[1] 1min(community_data$hwi_fdiv_normal)[1] 0max(community_data$hwi_fdiv_normal)[1] 1min(community_data$mass_fdiv_normal)[1] 0max(community_data$mass_fdiv_normal)[1] 1Join on realms
city_to_realm = read_csv(filename(CITY_DATA_OUTPUT_DIR, 'realms.csv'))
community_data_with_realm = left_join(community_data, city_to_realm)Cities as points
city_points = st_centroid(read_sf(filename(CITY_DATA_OUTPUT_DIR, 'city_selection.shp'))) %>% left_join(community_data_with_realm)
city_points_coords = st_coordinates(city_points)
city_points$latitude = city_points_coords[,1]
city_points$longitude = city_points_coords[,2]world_map = read_country_boundaries()Load community data, and create long format version
communities = read_csv(filename(COMMUNITY_OUTPUT_DIR, 'communities_for_analysis.csv'))
communitiescommunity_summary = communities %>% group_by(city_id) %>% summarise(regional_pool_size = n(), urban_pool_size = sum(relative_abundance_proxy > 0))
community_summaryLoad trait data
traits = read_csv(filename(TAXONOMY_OUTPUT_DIR, 'traits_ebird.csv'))
head(traits)Load spatial var
spatial_var = read_csv(filename(COMMUNITY_OUTPUT_DIR, 'spatial_var.csv')) %>% filter(city_id %in% community_summary$city_id)
spatial_varSummary metrics by Realm
test_required_values = function(name, df) {
cat(paste(
test_value_wilcox(paste(name, 'Std: MNTD'), df$mntd_standard),
test_value_wilcox(paste(name, 'Std: Beak Width FDiv'), df$beak_width_fdiv_standard),
test_value_wilcox(paste(name, 'Std: HWI FDiv'), df$hwi_fdiv_standard),
test_value_wilcox(paste(name, 'Std: Mass FDiv'), df$mass_fdiv_standard),
test_value_wilcox(paste(name, 'Norm: MNTD'), df$mntd_normal, mu = 0.5),
test_value_wilcox(paste(name, 'Norm: Beak Width FDiv'), df$beak_width_fdiv_normal, mu = 0.5),
test_value_wilcox(paste(name, 'Norm: HWI FDiv'), df$hwi_fdiv_normal, mu = 0.5),
test_value_wilcox(paste(name, 'Norm: Mass FDiv'), df$mass_fdiv_normal, mu = 0.5),
paste('N', nrow(df)),
sep = "\n"))
}test_required_values('Global', community_data_with_realm)Global Std: MNTD median -0.36 ***
Global Std: Beak Width FDiv median 0.03
Global Std: HWI FDiv median 0.39 **
Global Std: Mass FDiv median 0.29 ***
Global Norm: MNTD median 0.38 ***
Global Norm: Beak Width FDiv median 0.59 ***
Global Norm: HWI FDiv median 0.64 ***
Global Norm: Mass FDiv median 0.64 ***
N 308unique(community_data_with_realm$core_realm)[1] "Nearctic" "Neotropic" "Palearctic" "Afrotropic" "Indomalayan" "Australasia"test_required_values('Nearctic', community_data_with_realm[community_data_with_realm$core_realm == 'Nearctic',])Nearctic Std: MNTD median 0.67 *
Nearctic Std: Beak Width FDiv median 0.29
Nearctic Std: HWI FDiv median -0.8 ***
Nearctic Std: Mass FDiv median -0.26
Nearctic Norm: MNTD median 0.75
Nearctic Norm: Beak Width FDiv median 0.62
Nearctic Norm: HWI FDiv median 0.23 **
Nearctic Norm: Mass FDiv median 0.48
N 46test_required_values('Neotropic', community_data_with_realm[community_data_with_realm$core_realm == 'Neotropic',])Neotropic Std: MNTD median 0.03
Neotropic Std: Beak Width FDiv median -0.44 ***
Neotropic Std: HWI FDiv median -0.31
Neotropic Std: Mass FDiv median 0.33 *
Neotropic Norm: MNTD median 0.45
Neotropic Norm: Beak Width FDiv median 0.46
Neotropic Norm: HWI FDiv median 0.5
Neotropic Norm: Mass FDiv median 0.65 ***
N 64test_required_values('Palearctic', community_data_with_realm[community_data_with_realm$core_realm == 'Palearctic',])Palearctic Std: MNTD median 0.13
Palearctic Std: Beak Width FDiv median 1.25 ***
Palearctic Std: HWI FDiv median -0.39
Palearctic Std: Mass FDiv median 0.01
Palearctic Norm: MNTD median 0.57 *
Palearctic Norm: Beak Width FDiv median 0.93 ***
Palearctic Norm: HWI FDiv median 0.38
Palearctic Norm: Mass FDiv median 0.55
N 72test_required_values('Afrotropic', community_data_with_realm[community_data_with_realm$core_realm == 'Afrotropic',])Afrotropic Std: MNTD median -1.27 *
Afrotropic Std: Beak Width FDiv median -0.5
Afrotropic Std: HWI FDiv median 0.17
Afrotropic Std: Mass FDiv median -0.95
Afrotropic Norm: MNTD median 0.07 *
Afrotropic Norm: Beak Width FDiv median 0.42
Afrotropic Norm: HWI FDiv median 0.54
Afrotropic Norm: Mass FDiv median 0.34
N 9test_required_values('Indomalayan', community_data_with_realm[community_data_with_realm$core_realm == 'Indomalayan',])Indomalayan Std: MNTD median -0.63 ***
Indomalayan Std: Beak Width FDiv median -0.7 ***
Indomalayan Std: HWI FDiv median 1.11 ***
Indomalayan Std: Mass FDiv median 0.84 ***
Indomalayan Norm: MNTD median 0.21 ***
Indomalayan Norm: Beak Width FDiv median 0.46
Indomalayan Norm: HWI FDiv median 0.88 ***
Indomalayan Norm: Mass FDiv median 0.8 ***
N 111test_required_values('Australasia', community_data_with_realm[community_data_with_realm$core_realm == 'Australasia',])Australasia Std: MNTD median -1.39
Australasia Std: Beak Width FDiv median -0.74
Australasia Std: HWI FDiv median 0.78
Australasia Std: Mass FDiv median -0.94
Australasia Norm: MNTD median 0.09
Australasia Norm: Beak Width FDiv median 0.46
Australasia Norm: HWI FDiv median 0.79
Australasia Norm: Mass FDiv median 0.55
N 6How significant are those differences:
print('Standard')[1] "Standard"kruskal.test(mntd_standard~core_realm, data = community_data_with_realm)
Kruskal-Wallis rank sum test
data: mntd_standard by core_realm
Kruskal-Wallis chi-squared = 102.11, df = 5, p-value < 0.00000000000000022pairwise.wilcox.test(community_data_with_realm$mntd_standard, community_data_with_realm$core_realm)
Pairwise comparisons using Wilcoxon rank sum exact test
data: community_data_with_realm$mntd_standard and community_data_with_realm$core_realm
Afrotropic Australasia Indomalayan Nearctic Neotropic
Australasia 1.00000 - - - -
Indomalayan 0.00489 0.01024 - - -
Nearctic 0.0000053220919 0.00018 0.0000000141740 - -
Neotropic 0.00018 0.00170 0.0000000083499 0.21142 -
Palearctic 0.00018 0.00170 0.0000000000087 0.04492 1.00000
P value adjustment method: holm print('Normal')[1] "Normal"kruskal.test(mntd_normal~core_realm, data = community_data_with_realm)
Kruskal-Wallis rank sum test
data: mntd_normal by core_realm
Kruskal-Wallis chi-squared = 122.2, df = 5, p-value < 0.00000000000000022pairwise.wilcox.test(community_data_with_realm$mntd_normal, community_data_with_realm$core_realm)
Pairwise comparisons using Wilcoxon rank sum exact test
data: community_data_with_realm$mntd_normal and community_data_with_realm$core_realm
Afrotropic Australasia Indomalayan Nearctic Neotropic
Australasia 0.27213 - - - -
Indomalayan 0.00013 0.00267 - - -
Nearctic 0.00276 0.01815 0.0000009331952 - -
Neotropic 0.0000199762545 0.00053 0.0000000000089 0.01638 -
Palearctic 0.0000540321246 0.00147 < 0.0000000000000002 0.10733 0.00947
P value adjustment method: holm print('Standard')[1] "Standard"kruskal.test(beak_width_fdiv_standard~core_realm, data = community_data_with_realm)
Kruskal-Wallis rank sum test
data: beak_width_fdiv_standard by core_realm
Kruskal-Wallis chi-squared = 107.95, df = 5, p-value < 0.00000000000000022pairwise.wilcox.test(community_data_with_realm$beak_width_fdiv_standard, community_data_with_realm$core_realm)
Pairwise comparisons using Wilcoxon rank sum exact test
data: community_data_with_realm$beak_width_fdiv_standard and community_data_with_realm$core_realm
Afrotropic Australasia Indomalayan Nearctic Neotropic
Australasia 1.00000 - - - -
Indomalayan 1.00000 1.00000 - - -
Nearctic 0.11361 1.00000 0.00123 - -
Neotropic 1.00000 1.00000 1.00000 0.00388 -
Palearctic 0.00026 0.13449 < 0.0000000000000002 0.000001461233727 0.000000000000018
P value adjustment method: holm print('Normal')[1] "Normal"kruskal.test(beak_width_fdiv_standard~core_realm, data = community_data_with_realm)
Kruskal-Wallis rank sum test
data: beak_width_fdiv_standard by core_realm
Kruskal-Wallis chi-squared = 107.95, df = 5, p-value < 0.00000000000000022pairwise.wilcox.test(community_data_with_realm$beak_width_fdiv_standard, community_data_with_realm$core_realm)
Pairwise comparisons using Wilcoxon rank sum exact test
data: community_data_with_realm$beak_width_fdiv_standard and community_data_with_realm$core_realm
Afrotropic Australasia Indomalayan Nearctic Neotropic
Australasia 1.00000 - - - -
Indomalayan 1.00000 1.00000 - - -
Nearctic 0.11361 1.00000 0.00123 - -
Neotropic 1.00000 1.00000 1.00000 0.00388 -
Palearctic 0.00026 0.13449 < 0.0000000000000002 0.000001461233727 0.000000000000018
P value adjustment method: holm print('Standard')[1] "Standard"kruskal.test(hwi_fdiv_standard~core_realm, data = community_data_with_realm)
Kruskal-Wallis rank sum test
data: hwi_fdiv_standard by core_realm
Kruskal-Wallis chi-squared = 115.62, df = 5, p-value < 0.00000000000000022pairwise.wilcox.test(community_data_with_realm$hwi_fdiv_standard, community_data_with_realm$core_realm)
Pairwise comparisons using Wilcoxon rank sum exact test
data: community_data_with_realm$hwi_fdiv_standard and community_data_with_realm$core_realm
Afrotropic Australasia Indomalayan Nearctic Neotropic
Australasia 1.0000 - - - -
Indomalayan 0.1471 1.0000 - - -
Nearctic 1.0000 0.0038 0.00000000000000043 - -
Neotropic 1.0000 0.0680 0.00000000000000197 0.0925 -
Palearctic 1.0000 0.3068 0.00000000003605628 0.2673 1.0000
P value adjustment method: holm print('Normal')[1] "Normal"kruskal.test(hwi_fdiv_normal~core_realm, data = community_data_with_realm)
Kruskal-Wallis rank sum test
data: hwi_fdiv_normal by core_realm
Kruskal-Wallis chi-squared = 104.9, df = 5, p-value < 0.00000000000000022pairwise.wilcox.test(community_data_with_realm$hwi_fdiv_normal, community_data_with_realm$core_realm)
Pairwise comparisons using Wilcoxon rank sum exact test
data: community_data_with_realm$hwi_fdiv_normal and community_data_with_realm$core_realm
Afrotropic Australasia Indomalayan Nearctic Neotropic
Australasia 0.7233 - - - -
Indomalayan 0.1144 1.0000 - - -
Nearctic 0.0881 0.0193 0.0000000000006844 - -
Neotropic 1.0000 0.0204 0.0000000000000014 0.0000964104748761 -
Palearctic 0.9971 0.2748 0.0000000522948450 0.0073 0.9953
P value adjustment method: holm print('Standard')[1] "Standard"kruskal.test(mass_fdiv_standard~core_realm, data = community_data_with_realm)
Kruskal-Wallis rank sum test
data: mass_fdiv_standard by core_realm
Kruskal-Wallis chi-squared = 48.784, df = 5, p-value = 0.000000002457pairwise.wilcox.test(community_data_with_realm$mass_fdiv_standard, community_data_with_realm$core_realm)
Pairwise comparisons using Wilcoxon rank sum exact test
data: community_data_with_realm$mass_fdiv_standard and community_data_with_realm$core_realm
Afrotropic Australasia Indomalayan Nearctic Neotropic
Australasia 1.0000 - - - -
Indomalayan 0.0033 0.1401 - - -
Nearctic 0.1401 1.0000 0.0023 - -
Neotropic 0.0158 0.2989 1.0000 0.1401 -
Palearctic 0.0873 1.0000 0.00000002 1.0000 0.0658
P value adjustment method: holm print('Normal')[1] "Normal"kruskal.test(mass_fdiv_normal~core_realm, data = community_data_with_realm)
Kruskal-Wallis rank sum test
data: mass_fdiv_normal by core_realm
Kruskal-Wallis chi-squared = 58.138, df = 5, p-value = 0.00000000002946pairwise.wilcox.test(community_data_with_realm$mass_fdiv_normal, community_data_with_realm$core_realm)
Pairwise comparisons using Wilcoxon rank sum exact test
data: community_data_with_realm$mass_fdiv_normal and community_data_with_realm$core_realm
Afrotropic Australasia Indomalayan Nearctic Neotropic
Australasia 1.000 - - - -
Indomalayan 0.014 0.075 - - -
Nearctic 1.000 1.000 0.016 - -
Neotropic 0.126 1.000 0.014 0.958 -
Palearctic 0.824 1.000 0.0000000000008 1.000 0.014
P value adjustment method: holm What families exist in which realms?
communities %>%
left_join(city_to_realm) %>%
mutate(family = gsub( " .*$", "", ebird_species_name)) %>%
dplyr::select(family, core_realm) %>%
distinct() %>%
arrange(core_realm)Total urban families
communities %>%
mutate(family = gsub( " .*$", "", ebird_species_name)) %>%
dplyr::select(family) %>%
distinct() %>%
arrange()of which urban
communities %>%
filter(relative_abundance_proxy > 0) %>%
mutate(family = gsub( " .*$", "", ebird_species_name)) %>%
dplyr::select(family) %>%
distinct() %>%
arrange()Summary metrics by introduced species
communities = read_csv(filename(COMMUNITY_OUTPUT_DIR, 'communities_for_analysis.csv'))
city_introduced_species = communities %>% group_by(city_id) %>% summarise(number_of_species = n()) %>% left_join(
communities %>% group_by(city_id) %>% filter(origin == 'Introduced') %>% summarise(number_of_introduced_species = n())
) %>% replace_na(list(number_of_introduced_species = 0))
community_data_with_introductions = left_join(community_data, city_introduced_species)
community_data_with_introductions$has_introduced_species = community_data_with_introductions$number_of_introduced_species > 0
community_data_with_introductionscommunities %>%
filter(origin == 'Introduced') %>%
dplyr::select(ebird_species_name) %>%
group_by(ebird_species_name) %>%
summarise(total_cities = n()) %>%
arrange(desc(total_cities))community_data_with_introductions[,c('mntd_standard', 'has_introduced_species')]community_data_with_introductions %>% group_by(has_introduced_species) %>% summarise(
total_cities = n(),
mean_mntd_std = mean(mntd_standard, na.rm = T),
median_mntd_std = median(mntd_standard, na.rm = T),
sd_mntd_std = sd(mntd_standard, na.rm = T),
mean_mass_fdiv_std = mean(mass_fdiv_standard, na.rm = T),
median_mass_fdiv_std = median(mass_fdiv_standard, na.rm = T),
sd_mass_fdiv_std = sd(mass_fdiv_standard, na.rm = T),
mean_gape_width_fdiv_std = mean(beak_width_fdiv_standard, na.rm = T),
median_gape_width_fdiv_std = median(beak_width_fdiv_standard, na.rm = T),
sd_gape_width_fdiv_std = sd(beak_width_fdiv_standard, na.rm = T),
mean_handwing_index_fdiv_std = mean(hwi_fdiv_standard, na.rm = T),
median_handwing_index_fdiv_std = median(hwi_fdiv_standard, na.rm = T),
sd_handwing_index_fdiv_std = sd(hwi_fdiv_standard, na.rm = T)
)MNTD
ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = mntd_standard)) + geom_boxplot()
ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = mntd_normal)) + geom_boxplot()
wilcox.test(mntd_standard ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')
Wilcoxon rank sum test with continuity correction
data: mntd_standard by has_introduced_species
W = 7922, p-value = 0.00001262
alternative hypothesis: true location shift is not equal to 0There is a significant difference between the response of cities with introduced species (0.53±0.27) and those without (0.47±0.19) (p-value = 0.02).
wilcox.test(mntd_normal ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')
Wilcoxon rank sum test with continuity correction
data: mntd_normal by has_introduced_species
W = 7376.5, p-value = 0.0000003705
alternative hypothesis: true location shift is not equal to 0Mass FDiv
ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = mass_fdiv_standard)) + geom_boxplot()
ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = mass_fdiv_normal)) + geom_boxplot()
wilcox.test(mass_fdiv_standard ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')
Wilcoxon rank sum test with continuity correction
data: mass_fdiv_standard by has_introduced_species
W = 15009, p-value = 0.0000007626
alternative hypothesis: true location shift is not equal to 0There is a significant difference between the response of cities with introduced species (0.57±0.27) and those without (0.73±0.24) (p < 0.0001)
wilcox.test(mass_fdiv_normal ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')
Wilcoxon rank sum test with continuity correction
data: mass_fdiv_normal by has_introduced_species
W = 15204, p-value = 0.0000001989
alternative hypothesis: true location shift is not equal to 0Beak Gape FDiv
ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = beak_width_fdiv_standard)) + geom_boxplot()
ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = beak_width_fdiv_normal)) + geom_boxplot()
wilcox.test(beak_width_fdiv_standard ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')
Wilcoxon rank sum test with continuity correction
data: beak_width_fdiv_standard by has_introduced_species
W = 8659, p-value = 0.0006786
alternative hypothesis: true location shift is not equal to 0There is NOT a significant difference between the response of cities with introduced species (0.61±0.30) and those without (0.56±0.27)
wilcox.test(beak_width_fdiv_normal ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')
Wilcoxon rank sum test with continuity correction
data: beak_width_fdiv_normal by has_introduced_species
W = 9829.5, p-value = 0.06288
alternative hypothesis: true location shift is not equal to 0HWI FDiv
ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = hwi_fdiv_standard)) + geom_boxplot()
ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = hwi_fdiv_normal)) + geom_boxplot()
wilcox.test(hwi_fdiv_standard ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')
Wilcoxon rank sum test with continuity correction
data: hwi_fdiv_standard by has_introduced_species
W = 17622, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0There is a significant difference between the response of cities with introduced species (0.49±0.30) and those without (0.79±0.21) (p < 0.0001)
wilcox.test(hwi_fdiv_normal ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')
Wilcoxon rank sum test with continuity correction
data: hwi_fdiv_normal by has_introduced_species
W = 17673, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0What proportion of cities in each realm have introduced species
community_data_with_introductions %>% left_join(city_to_realm) %>%
group_by(core_realm) %>%
summarise(
total_cities = n(),
total_cities_with_introduced = sum(has_introduced_species),
total_cities_without_introduced = n() - sum(has_introduced_species)) %>%
arrange(core_realm)Are any introduced species not also present in a city?
communities %>%
filter(origin == 'Introduced') %>%
filter(relative_abundance_proxy < 0.1)What’s the average relative abundance of introduced species compared to native
communities %>%
group_by(origin) %>%
summarise(average_relative_abundance = mean(relative_abundance_proxy))communities %>%
group_by(origin) %>%
filter(relative_abundance_proxy > 0) %>%
summarise(average_relative_abundance = mean(relative_abundance_proxy))communities %>%
group_by(origin) %>%
summarise(average_relative_abundance = mean(relative_abundance_proxy))Create analysis data frame
geography = read_csv(filename(CITY_DATA_OUTPUT_DIR, 'geography.csv'))
names(geography) [1] "city_id" "city_avg_ndvi" "city_avg_elevation" "city_avg_temp"
[5] "city_avg_min_monthly_temp" "city_avg_max_monthly_temp" "city_avg_monthly_temp" "city_avg_rainfall"
[9] "city_avg_max_monthly_rainfall" "city_avg_min_monthly_rainfall" "city_avg_soil_moisture" "city_max_elev"
[13] "city_min_elev" "city_elev_range" "region_20km_avg_ndvi" "region_20km_avg_elevation"
[17] "region_20km_avg_soil_moisture" "region_20km_max_elev" "region_20km_min_elev" "region_20km_elev_range"
[21] "region_50km_avg_ndvi" "region_50km_avg_elevation" "region_50km_avg_soil_moisture" "region_50km_max_elev"
[25] "region_50km_min_elev" "region_50km_elev_range" analysis_data = community_data_with_realm[,c('city_id',
'mntd_standard', 'mass_fdiv_standard', 'beak_width_fdiv_standard', 'hwi_fdiv_standard',
'mntd_normal', 'mass_fdiv_normal', 'beak_width_fdiv_normal', 'hwi_fdiv_normal',
'core_realm')] %>%
left_join(city_points[,c('city_id', 'latitude', 'longitude')]) %>%
left_join(community_data_with_introductions[,c('city_id', 'has_introduced_species')]) %>%
left_join(geography) %>%
left_join(spatial_var)
analysis_data$abs_latitude = abs(analysis_data$latitude)
analysis_data$core_realm = factor(analysis_data$core_realm, levels = c('Palearctic', 'Nearctic', 'Neotropic', 'Afrotropic', 'Indomalayan', 'Australasia', 'Oceania'))
analysis_data$has_introduced_species = factor(analysis_data$has_introduced_species, level = c('FALSE', 'TRUE'), labels = c('No introduced species', 'Introduced species'))model_data = function(df, dependant_var) {
df[,c(dependant_var, 'core_realm', 'abs_latitude', 'longitude', 'has_introduced_species', 'city_avg_ndvi', 'city_avg_elevation', 'city_avg_temp', 'city_avg_min_monthly_temp', 'city_avg_max_monthly_temp', 'city_avg_monthly_temp', 'city_avg_rainfall', 'city_avg_max_monthly_rainfall', 'city_avg_min_monthly_rainfall', 'city_avg_soil_moisture', 'city_max_elev', 'city_min_elev', 'city_elev_range', 'region_20km_avg_ndvi', 'region_20km_avg_elevation', 'region_20km_avg_soil_moisture', 'region_20km_max_elev', 'region_20km_min_elev', 'region_20km_elev_range', 'region_50km_avg_ndvi', 'region_50km_avg_elevation', 'region_50km_avg_soil_moisture', 'region_50km_max_elev', 'region_50km_min_elev', 'region_50km_elev_range')]
}
model_data(analysis_data, 'mntd_standard')model_data(analysis_data, 'mntd_normal')names(analysis_data) [1] "city_id" "mntd_standard" "mass_fdiv_standard" "beak_width_fdiv_standard"
[5] "hwi_fdiv_standard" "mntd_normal" "mass_fdiv_normal" "beak_width_fdiv_normal"
[9] "hwi_fdiv_normal" "core_realm" "latitude" "longitude"
[13] "geometry" "has_introduced_species" "city_avg_ndvi" "city_avg_elevation"
[17] "city_avg_temp" "city_avg_min_monthly_temp" "city_avg_max_monthly_temp" "city_avg_monthly_temp"
[21] "city_avg_rainfall" "city_avg_max_monthly_rainfall" "city_avg_min_monthly_rainfall" "city_avg_soil_moisture"
[25] "city_max_elev" "city_min_elev" "city_elev_range" "region_20km_avg_ndvi"
[29] "region_20km_avg_elevation" "region_20km_avg_soil_moisture" "region_20km_max_elev" "region_20km_min_elev"
[33] "region_20km_elev_range" "region_50km_avg_ndvi" "region_50km_avg_elevation" "region_50km_avg_soil_moisture"
[37] "region_50km_max_elev" "region_50km_min_elev" "region_50km_elev_range" "NMDS1"
[41] "NMDS2" "abs_latitude" NMDS Spatial Helpers
analysis_data_nmds_coords = analysis_data[,c('NMDS1', 'NMDS2')]
coordinates(analysis_data_nmds_coords) = ~ NMDS1 + NMDS2
analysis_data_nmds_nearneigh <- knearneigh(analysis_data_nmds_coords)
analysis_data_nmds_neighbours <- knn2nb(analysis_data_nmds_nearneigh)Polygons around realms in NMDS plot
cities_to_realms_nmds = read_csv(filename(CITY_DATA_OUTPUT_DIR, 'realms.csv')) %>% left_join(analysis_data) %>% filter(!is.na(NMDS1))
unique(cities_to_realms_nmds$core_realm)[1] "Nearctic" "Neotropic" "Palearctic" "Afrotropic" "Indomalayan" "Australasia"realm_nmds_neartic_polygon = cities_to_realms_nmds %>% filter(core_realm == 'Nearctic') %>% slice(chull(NMDS1, NMDS2))
realm_nmds_neotropic_polygon = cities_to_realms_nmds %>% filter(core_realm == 'Neotropic') %>% slice(chull(NMDS1, NMDS2))
realm_nmds_palearctic_polygon = cities_to_realms_nmds %>% filter(core_realm == 'Palearctic') %>% slice(chull(NMDS1, NMDS2))
realm_nmds_afrotropic_polygon = cities_to_realms_nmds %>% filter(core_realm == 'Afrotropic') %>% slice(chull(NMDS1, NMDS2))
realm_nmds_indomalayan_polygon = cities_to_realms_nmds %>% filter(core_realm == 'Indomalayan') %>% slice(chull(NMDS1, NMDS2))
realm_nmds_australasia_polygon = cities_to_realms_nmds %>% filter(core_realm == 'Australasia') %>% slice(chull(NMDS1, NMDS2))
polygon_line_type = 'dashed'
polygon_linewidth = 0.4
with_realms_nmds = function(g) {
g +
geom_polygon(data = realm_nmds_neartic_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
geom_polygon(data = realm_nmds_neotropic_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
geom_polygon(data = realm_nmds_palearctic_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
geom_polygon(data = realm_nmds_afrotropic_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
geom_polygon(data = realm_nmds_indomalayan_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
geom_polygon(data = realm_nmds_australasia_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0)
}Lat Long Spatial Helpers
analysis_data_latlong_coords = analysis_data[,c('longitude', 'latitude')]
coordinates(analysis_data_latlong_coords) = ~ longitude + latitude
analysis_data_coords_nearneigh <- knearneigh(analysis_data_latlong_coords, longlat = TRUE)
analysis_data_neighbours <- knn2nb(analysis_data_coords_nearneigh)Polygons around realms in lat long plot
cities_to_realms_latlong = read_csv(filename(CITY_DATA_OUTPUT_DIR, 'realms.csv')) %>% left_join(analysis_data) %>% filter(!is.na(latitude))
unique(cities_to_realms_latlong$core_realm)[1] "Nearctic" "Neotropic" "Palearctic" "Afrotropic" "Indomalayan" "Australasia"realm_latlong_neartic_polygon = cities_to_realms_latlong %>% filter(core_realm == 'Nearctic') %>% slice(chull(latitude, longitude))
realm_latlong_neotropic_polygon = cities_to_realms_latlong %>% filter(core_realm == 'Neotropic') %>% slice(chull(latitude, longitude))
realm_latlong_palearctic_polygon = cities_to_realms_latlong %>% filter(core_realm == 'Palearctic') %>% slice(chull(latitude, longitude))
realm_latlong_afrotropic_polygon = cities_to_realms_latlong %>% filter(core_realm == 'Afrotropic') %>% slice(chull(latitude, longitude))
realm_latlong_indomalayan_polygon = cities_to_realms_latlong %>% filter(core_realm == 'Indomalayan') %>% slice(chull(latitude, longitude))
realm_latlong_australasia_polygon = cities_to_realms_latlong %>% filter(core_realm == 'Australasia') %>% slice(chull(latitude, longitude))
with_realms_latlong = function(g) {
g +
geom_polygon(data = realm_latlong_neartic_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
geom_polygon(data = realm_latlong_neotropic_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
geom_polygon(data = realm_latlong_palearctic_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
geom_polygon(data = realm_latlong_afrotropic_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
geom_polygon(data = realm_latlong_indomalayan_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
geom_polygon(data = realm_latlong_australasia_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0)
}Check for spatial autocorrelation
MNTD
Lat/Long
with_realms_latlong(ggplot(analysis_data, aes(x = latitude, y = longitude, colour = mntd_standard)) + geom_point() + standardised_colours_scale + labs(colour = "Standardised response"))
moran.test(analysis_data$mntd_standard, nb2listw(analysis_data_neighbours))
Moran I test under randomisation
data: analysis_data$mntd_standard
weights: nb2listw(analysis_data_neighbours)
Moran I statistic standard deviate = 7.343, p-value = 0.0000000000001044
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.529792986 -0.003257329 0.005269694 with_realms_latlong(ggplot(analysis_data, aes(x = latitude, y = longitude, colour = mntd_normal)) + geom_point() + normalised_colours_scale + labs(colour = "Normalised response"))
moran.test(analysis_data$mntd_normal, nb2listw(analysis_data_neighbours))
Moran I test under randomisation
data: analysis_data$mntd_normal
weights: nb2listw(analysis_data_neighbours)
Moran I statistic standard deviate = 7.6496, p-value = 0.00000000000001008
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.552360477 -0.003257329 0.005275586 NMDS
with_realms_nmds(ggplot(analysis_data, aes(x = NMDS1, y = NMDS2, colour = mntd_standard)) + geom_point() + standardised_colours_scale + labs(colour = "Standardised response"))
moran.test(analysis_data$mntd_standard, nb2listw(analysis_data_nmds_neighbours))
Moran I test under randomisation
data: analysis_data$mntd_standard
weights: nb2listw(analysis_data_nmds_neighbours)
Moran I statistic standard deviate = 6.5193, p-value = 0.00000000003531
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.445538119 -0.003257329 0.004739028 with_realms_nmds(ggplot(analysis_data, aes(x = NMDS1, y = NMDS2, colour = mntd_normal)) + geom_point() + standardised_colours_scale + labs(colour = "Normalised response"))
moran.test(analysis_data$mntd_normal, nb2listw(analysis_data_nmds_neighbours))
Moran I test under randomisation
data: analysis_data$mntd_normal
weights: nb2listw(analysis_data_nmds_neighbours)
Moran I statistic standard deviate = 6.2392, p-value = 0.0000000002199
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.426489674 -0.003257329 0.004744227 FDiv Beak Width
Lat/Long
with_realms_latlong(ggplot(analysis_data, aes(x = latitude, y = longitude, colour = beak_width_fdiv_standard)) + geom_point() + standardised_colours_scale + labs(colour = "Standardised response"))
moran.test(analysis_data$beak_width_fdiv_standard, nb2listw(analysis_data_neighbours))
Moran I test under randomisation
data: analysis_data$beak_width_fdiv_standard
weights: nb2listw(analysis_data_neighbours)
Moran I statistic standard deviate = 7.7208, p-value = 0.000000000000005781
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.557818587 -0.003257329 0.005281064 with_realms_latlong(ggplot(analysis_data, aes(x = latitude, y = longitude, colour = beak_width_fdiv_normal)) + geom_point() + standardised_colours_scale + labs(colour = "Normalised response"))
moran.test(analysis_data$beak_width_fdiv_normal, nb2listw(analysis_data_neighbours))
Moran I test under randomisation
data: analysis_data$beak_width_fdiv_normal
weights: nb2listw(analysis_data_neighbours)
Moran I statistic standard deviate = 8.3245, p-value < 0.00000000000000022
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.601742587 -0.003257329 0.005281967 NMDS
with_realms_nmds(ggplot(analysis_data, aes(x = NMDS1, y = NMDS2, colour = beak_width_fdiv_standard)) + geom_point() + standardised_colours_scale + labs(colour = "Standardised response"))
moran.test(analysis_data$beak_width_fdiv_standard, nb2listw(analysis_data_nmds_neighbours))
Moran I test under randomisation
data: analysis_data$beak_width_fdiv_standard
weights: nb2listw(analysis_data_nmds_neighbours)
Moran I statistic standard deviate = 8.0587, p-value = 0.0000000000000003855
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.552096148 -0.003257329 0.004749061 with_realms_nmds(ggplot(analysis_data, aes(x = NMDS1, y = NMDS2, colour = beak_width_fdiv_normal)) + geom_point() + standardised_colours_scale + labs(colour = "Normalised response"))
moran.test(analysis_data$beak_width_fdiv_normal, nb2listw(analysis_data_nmds_neighbours))
Moran I test under randomisation
data: analysis_data$beak_width_fdiv_normal
weights: nb2listw(analysis_data_nmds_neighbours)
Moran I statistic standard deviate = 7.5255, p-value = 0.00000000000002625
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.515397518 -0.003257329 0.004749858 FDiv HWI
Lat/Long
with_realms_latlong(ggplot(analysis_data, aes(x = latitude, y = longitude, colour = hwi_fdiv_standard)) + geom_point() + standardised_colours_scale + labs(colour = "Standardised response"))
moran.test(analysis_data$hwi_fdiv_standard, nb2listw(analysis_data_neighbours))
Moran I test under randomisation
data: analysis_data$hwi_fdiv_standard
weights: nb2listw(analysis_data_neighbours)
Moran I statistic standard deviate = 9.756, p-value < 0.00000000000000022
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.705943030 -0.003257329 0.005284408 with_realms_latlong(ggplot(analysis_data, aes(x = latitude, y = longitude, colour = hwi_fdiv_normal)) + geom_point() + standardised_colours_scale + labs(colour = "Normalised response"))
moran.test(analysis_data$hwi_fdiv_normal, nb2listw(analysis_data_neighbours))
Moran I test under randomisation
data: analysis_data$hwi_fdiv_normal
weights: nb2listw(analysis_data_neighbours)
Moran I statistic standard deviate = 10.186, p-value < 0.00000000000000022
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.737118550 -0.003257329 0.005283050 NMDS
with_realms_nmds(ggplot(analysis_data, aes(x = NMDS1, y = NMDS2, colour = hwi_fdiv_standard)) + geom_point() + standardised_colours_scale + labs(colour = "Standardised response"))
moran.test(analysis_data$hwi_fdiv_standard, nb2listw(analysis_data_nmds_neighbours))
Moran I test under randomisation
data: analysis_data$hwi_fdiv_standard
weights: nb2listw(analysis_data_nmds_neighbours)
Moran I statistic standard deviate = 5.7294, p-value = 0.000000005039
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.391697713 -0.003257329 0.004752012 with_realms_nmds(ggplot(analysis_data, aes(x = NMDS1, y = NMDS2, colour = hwi_fdiv_normal)) + geom_point() + standardised_colours_scale + labs(colour = "Normalised response"))
moran.test(analysis_data$hwi_fdiv_normal, nb2listw(analysis_data_nmds_neighbours))
Moran I test under randomisation
data: analysis_data$hwi_fdiv_normal
weights: nb2listw(analysis_data_nmds_neighbours)
Moran I statistic standard deviate = 6.2803, p-value = 0.0000000001689
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.429622019 -0.003257329 0.004750814 FDiv Mass
Lat/Long
with_realms_latlong(ggplot(analysis_data, aes(x = latitude, y = longitude, colour = mass_fdiv_standard)) + geom_point() + standardised_colours_scale + labs(colour = "Standardised response"))
moran.test(analysis_data$mass_fdiv_standard, nb2listw(analysis_data_neighbours))
Moran I test under randomisation
data: analysis_data$mass_fdiv_standard
weights: nb2listw(analysis_data_neighbours)
Moran I statistic standard deviate = 9.1521, p-value < 0.00000000000000022
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.661355126 -0.003257329 0.005273416 with_realms_latlong(ggplot(analysis_data, aes(x = latitude, y = longitude, colour = mass_fdiv_normal)) + geom_point() + standardised_colours_scale + labs(colour = "Normalised response"))
moran.test(analysis_data$mass_fdiv_normal, nb2listw(analysis_data_neighbours))
Moran I test under randomisation
data: analysis_data$mass_fdiv_normal
weights: nb2listw(analysis_data_neighbours)
Moran I statistic standard deviate = 9.008, p-value < 0.00000000000000022
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.650776207 -0.003257329 0.005271668 NMDS
with_realms_nmds(ggplot(analysis_data, aes(x = NMDS1, y = NMDS2, colour = mass_fdiv_standard)) + geom_point() + standardised_colours_scale + labs(colour = "Standardised response"))
moran.test(analysis_data$mass_fdiv_standard, nb2listw(analysis_data_nmds_neighbours))
Moran I test under randomisation
data: analysis_data$mass_fdiv_standard
weights: nb2listw(analysis_data_nmds_neighbours)
Moran I statistic standard deviate = 6.2114, p-value = 0.0000000002625
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.424488243 -0.003257329 0.004742312 with_realms_nmds(ggplot(analysis_data, aes(x = NMDS1, y = NMDS2, colour = mass_fdiv_normal)) + geom_point() + standardised_colours_scale + labs(colour = "Normalised response"))
moran.test(analysis_data$mass_fdiv_normal, nb2listw(analysis_data_nmds_neighbours))
Moran I test under randomisation
data: analysis_data$mass_fdiv_normal
weights: nb2listw(analysis_data_nmds_neighbours)
Moran I statistic standard deviate = 4.6538, p-value = 0.000001629
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.317170749 -0.003257329 0.004740769 Examine individual metrics
all_explanatories = c(
'city_avg_ndvi', 'city_avg_elevation', 'city_avg_temp',
'region_50km_avg_soil_moisture',
'core_realmAfrotropic', 'core_realmAustralasia', 'core_realmIndomalayan', 'core_realmNearctic', 'core_realmNeotropic', 'core_realmPalearctic',
'has_introduced_speciesNo introduced species', 'has_introduced_speciesIntroduced species'
)
all_explanatory_names = factor(
c(
'Avg. NDVI', 'Avg. Elevation', 'Avg. Temp.',
'Avg. Soil Moisture',
'Afrotropic', 'Australasia', 'Indomalayan', 'Nearctic', 'Neotropic', 'Palearctic',
'Introduced Absent', 'Introduced Present'
), ordered = T
)
explanatory_dictionary = data.frame(explanatory = all_explanatories, explanatory_name = all_explanatory_names)
with_explanatory_type_labels = function(p) {
p = p[p$explanatory != '(Intercept)',]
explanatory_levels = all_explanatories[all_explanatories %in% p$explanatory]
p$explanatory <- factor(p$explanatory, levels = explanatory_levels)
p$type <- 'Realm'
p$type[p$explanatory %in% c('city_avg_ndvi', 'city_avg_elevation', 'city_avg_temp')] <- 'City geography'
p$type[p$explanatory %in% c('region_50km_avg_soil_moisture')] <- 'Regional (50 km) geography'
p$type[p$explanatory %in% c('has_introduced_speciesNo introduced species', 'has_introduced_speciesIntroduced species')] <- 'Introduced species'
p
}
with_explanatory_names = function(p) {
p %>% left_join(explanatory_dictionary) %>% arrange(desc(explanatory_name))
}
type_labels = function(p) {
explanatory_levels = all_explanatories[all_explanatories %in% p$explanatory]
p$explanatory <- factor(p$explanatory, levels = explanatory_levels)
p$type <- 'Realm'
p$type[p$explanatory %in% c('city_avg_ndvi', 'city_avg_elevation', 'city_avg_temp', 'city_avg_min_monthly_temp', 'city_avg_max_monthly_temp',
'city_avg_monthly_temp', 'city_avg_rainfall', 'city_avg_max_monthly_rainfall', 'city_avg_min_monthly_rainfall',
'city_avg_soil_moisture', 'city_max_elev', 'city_min_elev', 'city_elev_range')] <- 'City geography'
p$type[p$explanatory %in% c('region_50km_avg_ndvi', 'region_50km_avg_elevation', 'region_50km_avg_soil_moisture', 'region_50km_max_elev',
'region_50km_min_elev', 'region_50km_elev_range')] <- 'Regional (50 km) geography'
p$type[p$explanatory %in% c('region_20km_avg_ndvi', 'region_20km_avg_elevation', 'region_20km_avg_soil_moisture', 'region_20km_max_elev',
'region_20km_min_elev', 'region_20km_elev_range')] <- 'Regional (20 km) geography'
p$type[p$explanatory %in% c('has_introduced_speciesNo introduced species', 'has_introduced_speciesIntroduced species')] <- 'Introduced species'
p
}explanatory_labels = c(
'has_introduced_species'='Introduced species',
'has_introduced_speciesNo introduced species'='Introduced absent',
'has_introduced_speciesIntroduced species'='Introduced present',
'city_avg_ndvi'='Average NDVI',
'city_avg_elevation'='Average elevation',
'city_avg_temp'='Average temperature',
'city_avg_min_monthly_temp'='Average minimum monthly temperature',
'city_avg_max_monthly_temp'='Average maximum monthly temperature',
'city_avg_monthly_temp'='Average monthly temperature',
'city_avg_rainfall'='Average rainfall',
'city_avg_max_monthly_rainfall'='Average maximum monthly rainfall',
'city_avg_min_monthly_rainfall'='Average minimum monthly rainfall',
'city_avg_soil_moisture'='Average soil moisture',
'city_max_elev'='Maximum elevation',
'city_min_elev'='Minimum elevation',
'city_elev_range'='Elevation range',
'region_20km_avg_ndvi'='Average NDVI',
'region_20km_avg_elevation'='Average elevation',
'region_20km_avg_soil_moisture'='Average soil moisture',
'region_20km_max_elev'='Maximum elevation',
'region_20km_min_elev'='Minimum elevation',
'region_20km_elev_range'='Elevation range',
'region_50km_avg_ndvi'='Average NDVI',
'region_50km_avg_elevation'='Average elevation',
'region_50km_avg_soil_moisture'='Average soil moisture',
'region_50km_max_elev'='Maximum elevation',
'region_50km_min_elev'='Minimum elevation',
'region_50km_elev_range'='Elevation range',
'abs_latitude' = 'Absolute latitude',
'latitude' = 'Latitude',
'longitude' = 'Longitude',
'core_realmAfrotropic' = 'Afrotropical',
'core_realmAustralasia' = 'Austaliasian',
'core_realmIndomalayan' = 'Indomalayan',
'core_realmNearctic' = 'Nearctic',
'core_realmNeotropic' = 'Neotropical',
'core_realmPalearctic' = 'Palearctic',
'core_realmOceania' = 'Oceanical')create_formula = function(response_var) {
as.formula(paste(response_var, '~ core_realm + city_avg_ndvi + city_avg_elevation + city_avg_temp + region_50km_avg_soil_moisture + has_introduced_species'))
}Helper plot functions
geom_map = function(map_sf, title, scale = standardised_colours_scale, colour_label = 'Standardised\nResponse') {
norm_mntd_analysis_geo = ggplot() +
geom_sf(data = world_map, aes(geometry = geometry)) +
map_sf +
scale +
labs(colour = colour_label) +
theme_bw() +
theme(legend.position="bottom")
}
geom_map_std = function(map_sf, title) {
geom_map(map_sf, title)
}
geom_map_nrm = function(map_sf, title) {
geom_map(map_sf, title, normalised_colours_scale, 'Normalised\nResponse')
}Helper Dredge functions
# Taken from MuMIN package
# https://rdrr.io/cran/MuMIn/src/R/averaging.R
# https://rdrr.io/cran/MuMIn/src/R/model.avg.R
.coefarr.avg <-
function(cfarr, weight, revised.var, full, alpha) {
weight <- weight / sum(weight)
nCoef <- dim(cfarr)[3L]
if(full) {
nas <- is.na(cfarr[, 1L, ]) & is.na(cfarr[, 2L, ])
cfarr[, 1L, ][nas] <- cfarr[, 2L, ][nas] <- 0
#cfarr[, 1L:2L, ][is.na(cfarr[, 1L:2L, ])] <- 0
if(!all(is.na(cfarr[, 3L, ])))
cfarr[ ,3L, ][is.na(cfarr[ , 3L, ])] <- Inf
}
avgcoef <- array(dim = c(nCoef, 5L),
dimnames = list(dimnames(cfarr)[[3L]], c("Estimate",
"Std. Error", "Adjusted SE", "Lower CI", "Upper CI")))
for(i in seq_len(nCoef))
avgcoef[i, ] <- par.avg(cfarr[, 1L, i], cfarr[, 2L, i], weight,
df = cfarr[, 3L, i], alpha = alpha, revised.var = revised.var)
avgcoef[is.nan(avgcoef)] <- NA
return(avgcoef)
}
.makecoefmat <- function(cf) {
no.ase <- all(is.na(cf[, 3L]))
z <- abs(cf[, 1L] / cf[, if(no.ase) 2L else 3L])
pval <- 2 * pnorm(z, lower.tail = FALSE)
cbind(cf[, if(no.ase) 1L:2L else 1L:3L, drop = FALSE],
`z value` = z, `Pr(>|z|)` = zapsmall(pval))
}
# Generate model selections using lmer, dredge, and model.avg
# `forumla` : a two-sided linear formula object describing both the fixed-effects and random-effects part of the model
# `data` : the data frame containing the variables from the formula
# `aic_delta` : the AIC delta to use for selecting models in model average
model_average <- function(formula, data, aic_delta = 20) {
model <- lm(
formula,
data=data
)
dredge_result <- dredge(model)
summary(model.avg(dredge_result, subset = delta < aic_delta))
}
# Create a summary data frame containing the selected variables from a model
# `model_sum` : The model summary output from `model_average`
model_summary <- function(model_sum) {
.column_name <- function(postfix) {
postfix
}
# just return the estimate and p value
weight <- model_sum$msTable[, 5L]
coefmat.full <- as.data.frame(.makecoefmat(.coefarr.avg(model_sum$coefArray, weight,
attr(model_sum, "revised.var"), TRUE, 0.05)))
coefmat.subset <-
as.data.frame(.makecoefmat(.coefarr.avg(model_sum$coefArray, weight,
attr(model_sum, "revised.var"), FALSE, 0.05)))
coefmat.subset <- coefmat.subset[-c(1), c(1, 2, 5)]
names(coefmat.subset) <- c(.column_name("estimate"), .column_name("error"), .column_name("p"))
coefmat.subset <- tibble::rownames_to_column(coefmat.subset, "explanatory")
coefmat.subset$model = 'subset'
coefmat.full <- coefmat.full[-c(1), c(1, 2, 5)]
names(coefmat.full) <- c(.column_name("estimate"), .column_name("error"), .column_name("p"))
coefmat.full <- tibble::rownames_to_column(coefmat.full, "explanatory")
coefmat.full$model = 'full'
rbind(coefmat.full, coefmat.subset)
}plot_dredge_result = function(result_table, mu = 0) {
p = result_table[result_table$model == 'full',]
p = type_labels(p)
ggplot(p, aes(y = explanatory, x = estimate, colour = type)) +
geom_line() +
geom_point() +
geom_errorbar(aes(xmin=estimate-error, xmax=estimate+error), width=.2,
position=position_dodge(0.05)) +
scale_y_discrete(
limits = rev(levels(p$explanatory)),
labels = explanatory_labels) +
scale_colour_manual(
values = c(realm_colour, city_geography_colour, regional_50km_geography_colour, regional_20km_geography_colour, introduced_species_colour),
breaks = c('Realm', 'City geography', 'Regional (50 km) geography', 'Regional (20 km) geography', 'Introduced species')) +
theme_bw() +
geom_vline(xintercept=mu, linetype="dotted") +
guides(colour=guide_legend(title="Predictor type")) + xlab('Difference in response from 0\nhabitat filtering (< 0) and competitive interactions (> 0)\n± Standard Error') + ylab('Predictor') +
theme(legend.justification = "top")
}GLS Spatial Helpers
gls_method = "ML"
spatial_model = function(formula, correlation) {
gls(
formula,
data = analysis_data,
correlation = correlation,
method = gls_method
)
}
plot_spatial_result = function(model_result) {
model_summary = summary(model_result)
result_table = as.data.frame(model_summary$tTable)
result_table$explanatory = rownames(result_table)
result_table = result_table %>% with_explanatory_type_labels() %>% with_explanatory_names()
ggplot2::ggplot(result_table, ggplot2::aes(y=factor(explanatory_name, level = all_explanatory_names, ordered = T), x=Value, colour = type)) +
ggplot2::geom_line() +
ggplot2::geom_point() +
ggplot2::geom_errorbar(ggplot2::aes(xmin=Value-Std.Error, xmax=Value+Std.Error), width=.2,
position=ggplot2::position_dodge(0.05)) +
ggplot2::theme_bw() +
ggplot2::geom_vline(xintercept=0, linetype="dotted") +
ggplot2::theme(legend.justification = "top") +
ylab('Predictor') +
guides(colour=guide_legend(title="Predictor type")) + xlab('Difference in response from 0\nhabitat filtering (< 0) and competitive interactions (> 0)\n± Standard Error') +
scale_colour_manual(
values = c(realm_colour, city_geography_colour, regional_50km_geography_colour, introduced_species_colour),
breaks = c('Realm', 'City geography', 'Regional (50 km) geography', 'Introduced species')) +
scale_y_discrete(limits = rev(all_explanatory_names[all_explanatory_names %in% result_table$explanatory_name]))
}Choose best spatial correlation function
MNTD
AIC(spatial_model(create_formula('mntd_standard'), corLin(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] 661.8901AIC(spatial_model(create_formula('mntd_standard'), corLin(form = ~ latitude + longitude)))[1] 666.4295AIC(spatial_model(create_formula('mntd_standard'), corExp(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] 653.0099AIC(spatial_model(create_formula('mntd_standard'), corExp(form = ~ latitude + longitude)))[1] 656.6118AIC(spatial_model(create_formula('mntd_standard'), corGaus(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] 657.7216AIC(spatial_model(create_formula('mntd_standard'), corGaus(form = ~ latitude + longitude)))[1] 662.5602AIC(spatial_model(create_formula('mntd_standard'), corRatio(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] 650.3759AIC(spatial_model(create_formula('mntd_standard'), corRatio(form = ~ latitude + longitude)))[1] 654.8339AIC(spatial_model(create_formula('mntd_standard'), corSpher(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] 659.8723AIC(spatial_model(create_formula('mntd_standard'), corSpher(form = ~ latitude + longitude)))[1] 664.1159MNTD: corRatio with NMDS + lat/long
AIC(spatial_model(create_formula('mntd_normal'), corLin(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] -81.16061AIC(spatial_model(create_formula('mntd_normal'), corLin(form = ~ latitude + longitude)))[1] -77.03513AIC(spatial_model(create_formula('mntd_normal'), corExp(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] -89.28852AIC(spatial_model(create_formula('mntd_normal'), corExp(form = ~ latitude + longitude)))[1] -86.17138AIC(spatial_model(create_formula('mntd_normal'), corGaus(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] -86.02288AIC(spatial_model(create_formula('mntd_normal'), corGaus(form = ~ latitude + longitude)))[1] -79.92235AIC(spatial_model(create_formula('mntd_normal'), corRatio(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] -92.46759AIC(spatial_model(create_formula('mntd_normal'), corRatio(form = ~ latitude + longitude)))[1] -86.60443AIC(spatial_model(create_formula('mntd_normal'), corSpher(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] -86.78558AIC(spatial_model(create_formula('mntd_normal'), corSpher(form = ~ latitude + longitude)))[1] -81.47393Beak Width
AIC(spatial_model(create_formula('beak_width_fdiv_standard'), corLin(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] 793.5251#AIC(spatial_model(create_formula('beak_width_fdiv_standard'), corLin(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('beak_width_fdiv_standard'), corExp(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] 789.282AIC(spatial_model(create_formula('beak_width_fdiv_standard'), corExp(form = ~ latitude + longitude)))[1] 790.0085AIC(spatial_model(create_formula('beak_width_fdiv_standard'), corGaus(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] 793.7668AIC(spatial_model(create_formula('beak_width_fdiv_standard'), corGaus(form = ~ latitude + longitude)))[1] 793.7656AIC(spatial_model(create_formula('beak_width_fdiv_standard'), corRatio(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] 791.2572AIC(spatial_model(create_formula('beak_width_fdiv_standard'), corRatio(form = ~ latitude + longitude)))[1] 791.3899AIC(spatial_model(create_formula('beak_width_fdiv_standard'), corSpher(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] 793.8106AIC(spatial_model(create_formula('beak_width_fdiv_standard'), corSpher(form = ~ latitude + longitude)))[1] 793.8106Beak width: corExp with NMDS + lat/long
AIC(spatial_model(create_formula('beak_width_fdiv_normal'), corLin(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] -45.30823AIC(spatial_model(create_formula('beak_width_fdiv_normal'), corLin(form = ~ latitude + longitude)))[1] -45.30823AIC(spatial_model(create_formula('beak_width_fdiv_normal'), corExp(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] -51.4648AIC(spatial_model(create_formula('beak_width_fdiv_normal'), corExp(form = ~ latitude + longitude)))[1] -50.46273AIC(spatial_model(create_formula('beak_width_fdiv_normal'), corGaus(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] -45.13365AIC(spatial_model(create_formula('beak_width_fdiv_normal'), corGaus(form = ~ latitude + longitude)))[1] -45.13552AIC(spatial_model(create_formula('beak_width_fdiv_normal'), corRatio(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] -47.44015AIC(spatial_model(create_formula('beak_width_fdiv_normal'), corRatio(form = ~ latitude + longitude)))[1] -47.368AIC(spatial_model(create_formula('beak_width_fdiv_normal'), corSpher(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] -44.97422AIC(spatial_model(create_formula('beak_width_fdiv_normal'), corSpher(form = ~ latitude + longitude)))[1] -44.97475HWI
AIC(spatial_model(create_formula('hwi_fdiv_standard'), corLin(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] 694.55AIC(spatial_model(create_formula('hwi_fdiv_standard'), corLin(form = ~ latitude + longitude)))[1] 699.3174AIC(spatial_model(create_formula('hwi_fdiv_standard'), corExp(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] 666.6631AIC(spatial_model(create_formula('hwi_fdiv_standard'), corExp(form = ~ latitude + longitude)))[1] 671.4924AIC(spatial_model(create_formula('hwi_fdiv_standard'), corGaus(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] 704.4854AIC(spatial_model(create_formula('hwi_fdiv_standard'), corGaus(form = ~ latitude + longitude)))[1] 709.2567AIC(spatial_model(create_formula('hwi_fdiv_standard'), corRatio(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] 685.5013AIC(spatial_model(create_formula('hwi_fdiv_standard'), corRatio(form = ~ latitude + longitude)))[1] 690.9801AIC(spatial_model(create_formula('hwi_fdiv_standard'), corSpher(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] 689.5034AIC(spatial_model(create_formula('hwi_fdiv_standard'), corSpher(form = ~ latitude + longitude)))[1] 707.7108HWI: corExp with NMDS + lat/long
#AIC(spatial_model(create_formula('hwi_fdiv_normal'), corLin(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
#AIC(spatial_model(create_formula('hwi_fdiv_normal'), corLin(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('hwi_fdiv_normal'), corExp(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] -96.05945AIC(spatial_model(create_formula('hwi_fdiv_normal'), corExp(form = ~ latitude + longitude)))[1] -91.16342AIC(spatial_model(create_formula('hwi_fdiv_normal'), corGaus(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] -54.57955AIC(spatial_model(create_formula('hwi_fdiv_normal'), corGaus(form = ~ latitude + longitude)))[1] -50.52492AIC(spatial_model(create_formula('hwi_fdiv_normal'), corRatio(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] -74.90875AIC(spatial_model(create_formula('hwi_fdiv_normal'), corRatio(form = ~ latitude + longitude)))[1] -69.65899AIC(spatial_model(create_formula('hwi_fdiv_normal'), corSpher(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] -53.60829AIC(spatial_model(create_formula('hwi_fdiv_normal'), corSpher(form = ~ latitude + longitude)))[1] -61.05117Mass
AIC(spatial_model(create_formula('mass_fdiv_standard'), corLin(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] 703.1192#AIC(spatial_model(create_formula('mass_fdiv_standard'), corLin(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('mass_fdiv_standard'), corExp(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] 753.3038AIC(spatial_model(create_formula('mass_fdiv_standard'), corExp(form = ~ latitude + longitude)))[1] 755.7536AIC(spatial_model(create_formula('mass_fdiv_standard'), corGaus(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] 786.6415AIC(spatial_model(create_formula('mass_fdiv_standard'), corGaus(form = ~ latitude + longitude)))[1] 785.0663AIC(spatial_model(create_formula('mass_fdiv_standard'), corRatio(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] 766.9612AIC(spatial_model(create_formula('mass_fdiv_standard'), corRatio(form = ~ latitude + longitude)))[1] 766.903AIC(spatial_model(create_formula('mass_fdiv_standard'), corSpher(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] 780.4268AIC(spatial_model(create_formula('mass_fdiv_standard'), corSpher(form = ~ latitude + longitude)))[1] 779.8638Mass: corExp with NMDS + lat/long
AIC(spatial_model(create_formula('mass_fdiv_normal'), corLin(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] -98.2492#AIC(spatial_model(create_formula('mass_fdiv_normal'), corLin(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('mass_fdiv_normal'), corExp(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] -49.779AIC(spatial_model(create_formula('mass_fdiv_normal'), corExp(form = ~ latitude + longitude)))[1] -48.0529AIC(spatial_model(create_formula('mass_fdiv_normal'), corGaus(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] -21.84722AIC(spatial_model(create_formula('mass_fdiv_normal'), corGaus(form = ~ latitude + longitude)))[1] -23.29214AIC(spatial_model(create_formula('mass_fdiv_normal'), corRatio(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] -35.17179AIC(spatial_model(create_formula('mass_fdiv_normal'), corRatio(form = ~ latitude + longitude)))[1] -35.60617AIC(spatial_model(create_formula('mass_fdiv_normal'), corSpher(form = ~ NMDS1 + NMDS2 + latitude + longitude)))[1] -27.00252AIC(spatial_model(create_formula('mass_fdiv_normal'), corSpher(form = ~ latitude + longitude)))[1] -27.91974correlation_formula = as.formula('~ NMDS1 + NMDS2 + latitude + longitude')
correlation_function_fdiv = function() {
corExp(form = correlation_formula)
}
correlation_function_mntd = function() {
corRatio(form = correlation_formula)
}MNTD
Standardised
std_mntd_analysis_geo_plot = geom_map_std(geom_sf(data = analysis_data, aes(color = mntd_standard, geometry = geometry)), 'MNTD')
std_mntd_analysis_geo_plot
std_mntd_analysis_data = model_data(analysis_data[!is.na(analysis_data$mntd_standard),], 'mntd_standard')
std_mntd_analysis_formula = create_formula('mntd_standard')
std_mntd_analysis_result = model_average(std_mntd_analysis_formula, std_mntd_analysis_data)
std_mntd_analysis_result_table = model_summary(std_mntd_analysis_result)
std_mntd_analysis_result_tablestd_mntd_analysis_pred_plot = plot_dredge_result(std_mntd_analysis_result_table)
std_mntd_analysis_pred_plot
Do the residuals still contain spatial autocorrelation from a fitted lm?
std_mntd_lm = lm(std_mntd_analysis_formula, std_mntd_analysis_data)
moran.test(std_mntd_lm$residuals, nb2listw(analysis_data_neighbours))
Moran I test under randomisation
data: std_mntd_lm$residuals
weights: nb2listw(analysis_data_neighbours)
Moran I statistic standard deviate = 3.8425, p-value = 0.0000609
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.275671194 -0.003257329 0.005269438 moran.test(std_mntd_lm$residuals, nb2listw(analysis_data_nmds_neighbours))
Moran I test under randomisation
data: std_mntd_lm$residuals
weights: nb2listw(analysis_data_nmds_neighbours)
Moran I statistic standard deviate = 2.9566, p-value = 0.001555
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.200271681 -0.003257329 0.004738802 std_mntd_spatial_model = spatial_model(std_mntd_analysis_formula, correlation_function_mntd())
moran.test(std_mntd_spatial_model$residuals, nb2listw(analysis_data_neighbours))
Moran I test under randomisation
data: std_mntd_spatial_model$residuals
weights: nb2listw(analysis_data_neighbours)
Moran I statistic standard deviate = 4.0485, p-value = 0.00002578
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.290664449 -0.003257329 0.005270844 moran.test(std_mntd_spatial_model$residuals, nb2listw(analysis_data_nmds_neighbours))
Moran I test under randomisation
data: std_mntd_spatial_model$residuals
weights: nb2listw(analysis_data_nmds_neighbours)
Moran I statistic standard deviate = 3.1544, p-value = 0.0008041
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.213916976 -0.003257329 0.004740043 std_mntd_analysis_pred_spatial_plot = plot_spatial_result(std_mntd_spatial_model)
std_mntd_analysis_pred_spatial_plot
Normalised
nrm_mntd_analysis_geo_plot = geom_map_nrm(geom_sf(data = analysis_data, aes(color = mntd_normal, geometry = geometry)), 'MNTD')
nrm_mntd_analysis_geo_plot
nrm_mntd_analysis_data = model_data(analysis_data[!is.na(analysis_data$mntd_normal),], 'mntd_normal')
nrm_mntd_analysis_formula = create_formula('mntd_normal')
nrm_mntd_analysis_result = model_average(nrm_mntd_analysis_formula, nrm_mntd_analysis_data)
nrm_mntd_analysis_result_table = model_summary(nrm_mntd_analysis_result)
nrm_mntd_analysis_result_tablenrm_mntd_analysis_pred_plot = plot_dredge_result(nrm_mntd_analysis_result_table)
nrm_mntd_analysis_pred_plot
Do the residuals still contain spatial autocorrelation from a fitted lm?
nrm_mntd_lm = lm(nrm_mntd_analysis_formula, nrm_mntd_analysis_data)
moran.test(nrm_mntd_lm$residuals, nb2listw(analysis_data_neighbours))
Moran I test under randomisation
data: nrm_mntd_lm$residuals
weights: nb2listw(analysis_data_neighbours)
Moran I statistic standard deviate = 4.2864, p-value = 0.000009079
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.307465451 -0.003257329 0.005254828 moran.test(nrm_mntd_lm$residuals, nb2listw(analysis_data_nmds_neighbours))
Moran I test under randomisation
data: nrm_mntd_lm$residuals
weights: nb2listw(analysis_data_nmds_neighbours)
Moran I statistic standard deviate = 2.0944, p-value = 0.01811
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.140725445 -0.003257329 0.004725908 nrm_mntd_spatial_model = spatial_model(nrm_mntd_analysis_formula, correlation_function_mntd())
moran.test(nrm_mntd_spatial_model$residuals, nb2listw(analysis_data_neighbours))
Moran I test under randomisation
data: nrm_mntd_spatial_model$residuals
weights: nb2listw(analysis_data_neighbours)
Moran I statistic standard deviate = 4.7455, p-value = 0.00000104
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.340740843 -0.003257329 0.005254693 moran.test(nrm_mntd_spatial_model$residuals, nb2listw(analysis_data_nmds_neighbours))
Moran I test under randomisation
data: nrm_mntd_spatial_model$residuals
weights: nb2listw(analysis_data_nmds_neighbours)
Moran I statistic standard deviate = 2.3578, p-value = 0.009192
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.158827607 -0.003257329 0.004725789 nrm_mntd_analysis_pred_spatial_plot = plot_spatial_result(nrm_mntd_spatial_model)
nrm_mntd_analysis_pred_spatial_plot
Gape width - FDiv
Standardised
std_gape_fdiv_analysis_geo_plot = geom_map_std(geom_sf(data = analysis_data, aes(color = beak_width_fdiv_standard, geometry = geometry)), 'Beak Width FDiv')
std_gape_fdiv_analysis_geo_plot
std_gape_fdiv_analysis_data = model_data(analysis_data[!is.na(analysis_data$beak_width_fdiv_standard),], 'beak_width_fdiv_standard')
std_gape_fdiv_analysis_formula = create_formula('beak_width_fdiv_standard')
std_gape_fdiv_analysis_result = model_average(std_gape_fdiv_analysis_formula, std_gape_fdiv_analysis_data)
std_gape_fdiv_analysis_result_table = model_summary(std_gape_fdiv_analysis_result)
std_gape_fdiv_analysis_result_tablestd_gape_fdiv_analysis_pred_plot = plot_dredge_result(std_gape_fdiv_analysis_result_table)
std_gape_fdiv_analysis_pred_plot
Do the residuals still contain spatial autocorrelation from a fitted lm?
std_gape_fdiv_lm = lm(std_gape_fdiv_analysis_formula, std_gape_fdiv_analysis_data)
moran.test(std_gape_fdiv_lm$residuals, nb2listw(analysis_data_neighbours))
Moran I test under randomisation
data: std_gape_fdiv_lm$residuals
weights: nb2listw(analysis_data_neighbours)
Moran I statistic standard deviate = 3.5059, p-value = 0.0002275
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.251017001 -0.003257329 0.005260172 moran.test(std_gape_fdiv_lm$residuals, nb2listw(analysis_data_nmds_neighbours))
Moran I test under randomisation
data: std_gape_fdiv_lm$residuals
weights: nb2listw(analysis_data_nmds_neighbours)
Moran I statistic standard deviate = 1.6582, p-value = 0.04864
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.110794578 -0.003257329 0.004730625 std_gape_fdiv_spatial_model = spatial_model(std_gape_fdiv_analysis_formula, correlation_function_fdiv())
moran.test(std_gape_fdiv_spatial_model$residuals, nb2listw(analysis_data_neighbours))
Moran I test under randomisation
data: std_gape_fdiv_spatial_model$residuals
weights: nb2listw(analysis_data_neighbours)
Moran I statistic standard deviate = 3.5695, p-value = 0.0001789
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.255641928 -0.003257329 0.005260840 moran.test(std_gape_fdiv_spatial_model$residuals, nb2listw(analysis_data_nmds_neighbours))
Moran I test under randomisation
data: std_gape_fdiv_spatial_model$residuals
weights: nb2listw(analysis_data_nmds_neighbours)
Moran I statistic standard deviate = 1.868, p-value = 0.03088
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.125233427 -0.003257329 0.004731214 std_gape_fdiv_analysis_pred_spatial_plot = plot_spatial_result(std_gape_fdiv_spatial_model)
std_gape_fdiv_analysis_pred_spatial_plot
Normalised
nrm_gape_fdiv_analysis_geo_plot = geom_map_nrm(geom_sf(data = analysis_data, aes(color = beak_width_fdiv_normal, geometry = geometry)), 'Beak Width FDiv')
nrm_gape_fdiv_analysis_geo_plot
nrm_gape_fdiv_analysis_data = model_data(analysis_data[!is.na(analysis_data$beak_width_fdiv_normal),], 'beak_width_fdiv_normal')
nrm_gape_fdiv_analysis_formula = create_formula('beak_width_fdiv_normal')
nrm_gape_fdiv_analysis_result = model_average(nrm_gape_fdiv_analysis_formula, nrm_gape_fdiv_analysis_data)
nrm_gape_fdiv_analysis_result_table = model_summary(nrm_gape_fdiv_analysis_result)
nrm_gape_fdiv_analysis_result_tablenrm_gape_fdiv_analysis_pred_plot = plot_dredge_result(nrm_gape_fdiv_analysis_result_table)
nrm_gape_fdiv_analysis_pred_plot
Do the residuals still contain spatial autocorrelation from a fitted lm?
nrm_gape_fdiv_lm = lm(nrm_gape_fdiv_analysis_formula, nrm_gape_fdiv_analysis_data)
moran.test(nrm_gape_fdiv_lm$residuals, nb2listw(analysis_data_neighbours))
Moran I test under randomisation
data: nrm_gape_fdiv_lm$residuals
weights: nb2listw(analysis_data_neighbours)
Moran I statistic standard deviate = 4.3357, p-value = 0.000007265
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.311050869 -0.003257329 0.005255227 moran.test(nrm_gape_fdiv_lm$residuals, nb2listw(analysis_data_nmds_neighbours))
Moran I test under randomisation
data: nrm_gape_fdiv_lm$residuals
weights: nb2listw(analysis_data_nmds_neighbours)
Moran I statistic standard deviate = 1.4217, p-value = 0.07756
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.094482089 -0.003257329 0.004726261 nrm_gape_fdiv_spatial_model = spatial_model(nrm_gape_fdiv_analysis_formula, correlation_function_fdiv())
moran.test(nrm_gape_fdiv_spatial_model$residuals, nb2listw(analysis_data_neighbours))
Moran I test under randomisation
data: nrm_gape_fdiv_spatial_model$residuals
weights: nb2listw(analysis_data_neighbours)
Moran I statistic standard deviate = 4.4995, p-value = 0.000003406
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.322936781 -0.003257329 0.005255637 moran.test(nrm_gape_fdiv_spatial_model$residuals, nb2listw(analysis_data_nmds_neighbours))
Moran I test under randomisation
data: nrm_gape_fdiv_spatial_model$residuals
weights: nb2listw(analysis_data_nmds_neighbours)
Moran I statistic standard deviate = 1.7347, p-value = 0.0414
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.116002600 -0.003257329 0.004726623 nrm_gape_fdiv_analysis_pred_spatial_plot = plot_spatial_result(nrm_gape_fdiv_spatial_model)
nrm_gape_fdiv_analysis_pred_spatial_plot
HWI - FDiv
Standardised
std_hwi_fdiv_analysis_geo_plot = geom_map_std(geom_sf(data = analysis_data, aes(color = hwi_fdiv_standard, geometry = geometry)), 'HWI FDiv')
std_hwi_fdiv_analysis_geo_plot
std_hwi_fdiv_analysis_data = model_data(analysis_data[!is.na(analysis_data$hwi_fdiv_standard),], 'hwi_fdiv_standard')
std_hwi_fdiv_analysis_formula = create_formula('hwi_fdiv_standard')
std_hwi_fdiv_analysis_result = model_average(std_hwi_fdiv_analysis_formula, std_hwi_fdiv_analysis_data)
std_hwi_fdiv_analysis_result_table = model_summary(std_hwi_fdiv_analysis_result)
std_hwi_fdiv_analysis_result_tablestd_hwi_fdiv_analysis_pred_plot = plot_dredge_result(std_hwi_fdiv_analysis_result_table)
std_hwi_fdiv_analysis_pred_plot
std_hwi_fdiv_lm = lm(std_hwi_fdiv_analysis_formula, std_hwi_fdiv_analysis_data)
moran.test(std_hwi_fdiv_lm$residuals, nb2listw(analysis_data_neighbours))
Moran I test under randomisation
data: std_hwi_fdiv_lm$residuals
weights: nb2listw(analysis_data_neighbours)
Moran I statistic standard deviate = 6.1738, p-value = 0.0000000003333
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.444286636 -0.003257329 0.005254904 moran.test(std_hwi_fdiv_lm$residuals, nb2listw(analysis_data_nmds_neighbours))
Moran I test under randomisation
data: std_hwi_fdiv_lm$residuals
weights: nb2listw(analysis_data_nmds_neighbours)
Moran I statistic standard deviate = 3.8009, p-value = 0.00007207
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.258041658 -0.003257329 0.004725975 std_hwi_fdiv_spatial_model = spatial_model(std_hwi_fdiv_analysis_formula, correlation_function_fdiv())
moran.test(std_hwi_fdiv_spatial_model$residuals, nb2listw(analysis_data_neighbours))
Moran I test under randomisation
data: std_hwi_fdiv_spatial_model$residuals
weights: nb2listw(analysis_data_neighbours)
Moran I statistic standard deviate = 7.2179, p-value = 0.0000000000002639
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.520272713 -0.003257329 0.005260863 moran.test(std_hwi_fdiv_spatial_model$residuals, nb2listw(analysis_data_nmds_neighbours))
Moran I test under randomisation
data: std_hwi_fdiv_spatial_model$residuals
weights: nb2listw(analysis_data_nmds_neighbours)
Moran I statistic standard deviate = 3.3854, p-value = 0.0003554
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.229604043 -0.003257329 0.004731234 std_hwi_fdiv_analysis_pred_spatial_plot = plot_spatial_result(std_hwi_fdiv_spatial_model)
std_hwi_fdiv_analysis_pred_spatial_plot
Normalised
nrm_hwi_fdiv_analysis_geo_plot = geom_map_nrm(geom_sf(data = analysis_data, aes(color = hwi_fdiv_normal, geometry = geometry)), 'HWI FDiv')
nrm_hwi_fdiv_analysis_geo_plot
nrm_hwi_fdiv_analysis_data = model_data(analysis_data[!is.na(analysis_data$hwi_fdiv_normal),], 'hwi_fdiv_normal')
nrm_hwi_fdiv_analysis_formula = create_formula('hwi_fdiv_normal')
nrm_hwi_fdiv_analysis_result = model_average(nrm_hwi_fdiv_analysis_formula, nrm_hwi_fdiv_analysis_data)
nrm_hwi_fdiv_analysis_result_table = model_summary(nrm_hwi_fdiv_analysis_result)
nrm_hwi_fdiv_analysis_result_tablenrm_hwi_fdiv_analysis_pred_plot = plot_dredge_result(nrm_hwi_fdiv_analysis_result_table)
nrm_hwi_fdiv_analysis_pred_plot
nrm_hwi_fdiv_lm = lm(nrm_hwi_fdiv_analysis_formula, nrm_hwi_fdiv_analysis_data)
moran.test(nrm_hwi_fdiv_lm$residuals, nb2listw(analysis_data_neighbours))
Moran I test under randomisation
data: nrm_hwi_fdiv_lm$residuals
weights: nb2listw(analysis_data_neighbours)
Moran I statistic standard deviate = 7.0294, p-value = 0.000000000001037
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.505183959 -0.003257329 0.005231738 moran.test(nrm_hwi_fdiv_lm$residuals, nb2listw(analysis_data_nmds_neighbours))
Moran I test under randomisation
data: nrm_hwi_fdiv_lm$residuals
weights: nb2listw(analysis_data_nmds_neighbours)
Moran I statistic standard deviate = 3.6271, p-value = 0.0001433
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.245547978 -0.003257329 0.004705532 nrm_hwi_fdiv_spatial_model = spatial_model(nrm_hwi_fdiv_analysis_formula, correlation_function_fdiv())
moran.test(nrm_hwi_fdiv_spatial_model$residuals, nb2listw(analysis_data_neighbours))
Moran I test under randomisation
data: nrm_hwi_fdiv_spatial_model$residuals
weights: nb2listw(analysis_data_neighbours)
Moran I statistic standard deviate = 8.1492, p-value < 0.00000000000000022
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.587846115 -0.003257329 0.005261332 moran.test(nrm_hwi_fdiv_spatial_model$residuals, nb2listw(analysis_data_nmds_neighbours))
Moran I test under randomisation
data: nrm_hwi_fdiv_spatial_model$residuals
weights: nb2listw(analysis_data_nmds_neighbours)
Moran I statistic standard deviate = 3.5151, p-value = 0.0002198
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.238537056 -0.003257329 0.004731648 nrm_hwi_fdiv_analysis_pred_spatial_plot = plot_spatial_result(nrm_hwi_fdiv_spatial_model)
nrm_hwi_fdiv_analysis_pred_spatial_plot
Mass - FDiv
Standardised
std_mass_fdiv_analysis_geo_plot = geom_map_std(geom_sf(data = analysis_data, aes(color = mass_fdiv_standard, geometry = geometry)), 'Mass FDiv')
std_mass_fdiv_analysis_geo_plot
std_mass_fdiv_analysis_data = model_data(analysis_data[!is.na(analysis_data$mass_fdiv_standard),], 'mass_fdiv_standard')
std_mass_fdiv_analysis_formula = create_formula('mass_fdiv_standard')
std_mass_fdiv_analysis_result <- model_average(std_mass_fdiv_analysis_formula, std_mass_fdiv_analysis_data)
std_mass_fdiv_analysis_result_table = model_summary(std_mass_fdiv_analysis_result)
std_mass_fdiv_analysis_result_tablestd_mass_fdiv_analysis_pred_plot = plot_dredge_result(std_mass_fdiv_analysis_result_table)
std_mass_fdiv_analysis_pred_plot
std_mass_fdiv_lm = lm(std_mass_fdiv_analysis_formula, std_mass_fdiv_analysis_data)
moran.test(std_mass_fdiv_lm$residuals, nb2listw(analysis_data_neighbours))
Moran I test under randomisation
data: std_mass_fdiv_lm$residuals
weights: nb2listw(analysis_data_neighbours)
Moran I statistic standard deviate = 7.4467, p-value = 0.00000000000004785
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.536475629 -0.003257329 0.005253273 moran.test(std_mass_fdiv_lm$residuals, nb2listw(analysis_data_nmds_neighbours))
Moran I test under randomisation
data: std_mass_fdiv_lm$residuals
weights: nb2listw(analysis_data_nmds_neighbours)
Moran I statistic standard deviate = 4.7957, p-value = 0.0000008107
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.326373879 -0.003257329 0.004724537 std_mass_fdiv_spatial_model = spatial_model(std_mass_fdiv_analysis_formula, correlation_function_fdiv())
moran.test(std_mass_fdiv_spatial_model$residuals, nb2listw(analysis_data_neighbours))
Moran I test under randomisation
data: std_mass_fdiv_spatial_model$residuals
weights: nb2listw(analysis_data_neighbours)
Moran I statistic standard deviate = 7.9698, p-value = 0.0000000000000007945
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.574414910 -0.003257329 0.005253707 moran.test(std_mass_fdiv_spatial_model$residuals, nb2listw(analysis_data_nmds_neighbours))
Moran I test under randomisation
data: std_mass_fdiv_spatial_model$residuals
weights: nb2listw(analysis_data_nmds_neighbours)
Moran I statistic standard deviate = 5.3014, p-value = 0.00000005745
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.361152294 -0.003257329 0.004724919 std_mass_fdiv_analysis_pred_spatial_plot = plot_spatial_result(std_mass_fdiv_spatial_model)
std_mass_fdiv_analysis_pred_spatial_plot
Normalised
nrm_mass_fdiv_analysis_geo_plot = geom_map_nrm(geom_sf(data = analysis_data, aes(color = mass_fdiv_normal, geometry = geometry)), 'Mass FDiv')
nrm_mass_fdiv_analysis_geo_plot
nrm_mass_fdiv_analysis_data = model_data(analysis_data[!is.na(analysis_data$mass_fdiv_normal),], 'mass_fdiv_normal')
nrm_mass_fdiv_analysis_formula = create_formula('mass_fdiv_normal')
nrm_mass_fdiv_analysis_result = model_average(nrm_mass_fdiv_analysis_formula, nrm_mass_fdiv_analysis_data)
nrm_mass_fdiv_analysis_result_table = model_summary(nrm_mass_fdiv_analysis_result)
nrm_mass_fdiv_analysis_result_tablenrm_mass_fdiv_analysis_pred_plot = plot_dredge_result(nrm_mass_fdiv_analysis_result_table)
nrm_mass_fdiv_analysis_pred_plot
nrm_mass_fdiv_lm = lm(nrm_mass_fdiv_analysis_formula, nrm_mass_fdiv_analysis_data)
moran.test(nrm_mass_fdiv_lm$residuals, nb2listw(analysis_data_neighbours))
Moran I test under randomisation
data: nrm_mass_fdiv_lm$residuals
weights: nb2listw(analysis_data_neighbours)
Moran I statistic standard deviate = 6.9903, p-value = 0.000000000001372
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.503538387 -0.003257329 0.005256267 moran.test(nrm_mass_fdiv_lm$residuals, nb2listw(analysis_data_nmds_neighbours))
Moran I test under randomisation
data: nrm_mass_fdiv_lm$residuals
weights: nb2listw(analysis_data_nmds_neighbours)
Moran I statistic standard deviate = 2.9195, p-value = 0.001753
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.197472475 -0.003257329 0.004727179 nrm_mass_fdiv_spatial_model = spatial_model(nrm_mass_fdiv_analysis_formula, correlation_function_fdiv())
moran.test(nrm_mass_fdiv_spatial_model$residuals, nb2listw(analysis_data_neighbours))
Moran I test under randomisation
data: nrm_mass_fdiv_spatial_model$residuals
weights: nb2listw(analysis_data_neighbours)
Moran I statistic standard deviate = 7.5406, p-value = 0.0000000000000234
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.543587169 -0.003257329 0.005259210 moran.test(nrm_mass_fdiv_spatial_model$residuals, nb2listw(analysis_data_nmds_neighbours))
Moran I test under randomisation
data: nrm_mass_fdiv_spatial_model$residuals
weights: nb2listw(analysis_data_nmds_neighbours)
Moran I statistic standard deviate = 3.2302, p-value = 0.0006185
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.218896280 -0.003257329 0.004729776 nrm_mass_fdiv_analysis_pred_spatial_plot = plot_spatial_result(nrm_mass_fdiv_spatial_model)
nrm_mass_fdiv_analysis_pred_spatial_plot
Create plot of differences in process response
pred_legend <- ggpubr::get_legend(
# create some space to the left of the legend
std_hwi_fdiv_analysis_pred_plot + theme(legend.box.margin = margin(0, 0, 0, 0)) + guides(colour=guide_legend(ncol=2)) + labs(color = "Predictor type")
)
geo_legend <- ggpubr::get_legend(
# create some space to the left of the legend
std_mass_fdiv_analysis_geo_plot + theme(legend.box.margin = margin(-80, 0, 0, 12), legend.title.position = "top", legend.key.width = unit(10, 'mm')) + labs(color = "Standardised response")
)
legend = plot_grid(
geo_legend,
pred_legend,
nrow = 1
)
legend
plot_grid(
plot_grid(
std_mntd_analysis_geo_plot + theme(legend.position="none"),
std_mntd_analysis_pred_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''),
nrow = 1
) + draw_label("MNTD", size = 16, angle = 90, x = 0.01, y = 0.5),
plot_grid(
std_gape_fdiv_analysis_geo_plot + theme(legend.position="none"),
std_gape_fdiv_analysis_pred_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''),
nrow = 1
) + draw_label("Beak Width", size = 16, angle = 90, x = 0.01, y = 0.5),
plot_grid(
std_hwi_fdiv_analysis_geo_plot + theme(legend.position="none"),
std_hwi_fdiv_analysis_pred_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''),
nrow = 1
) + draw_label("HWI", size = 16, angle = 90, x = 0.01, y = 0.5),
plot_grid(
std_mass_fdiv_analysis_geo_plot + theme(legend.position="none"),
std_mass_fdiv_analysis_pred_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''),
nrow = 1
) + draw_label("Mass", size = 16, angle = 90, x = 0.01, y = 0.5),
legend,
nrow = 5
)
ggsave(filename(FIGURES_OUTPUT_DIR, 'process_response.jpg'), width = 3000, height = 3200, units = 'px')
pred_fig_legend <- ggpubr::get_legend(
# create some space to the left of the legend
std_hwi_fdiv_analysis_pred_plot + theme(legend.box.margin = margin(0, 0, 0, -20)) + guides(colour=guide_legend(ncol=2)) + labs(color = "Predictor type")
)
geo_fig_legend <- ggpubr::get_legend(
# create some space to the left of the legend
std_mass_fdiv_analysis_geo_plot + theme(legend.box.margin = margin(0, 0, 0, 0), legend.title.position = "top", legend.key.width = unit(10, 'mm')) + labs(color = "Standardised response")
)
remove_x_scale = scale_x_continuous(name = '', limits = c(-3, 3))
theme_no_legend = theme(legend.position="none", panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"))
fig2 = grid.arrange(
# row 1 - titles
arrangeGrob(grid::textGrob('A) Standardised response by city', x = 0.1, hjust = 0, gp=gpar(fontface="bold"))),
arrangeGrob(grid::textGrob('B) Standardised response predictors', x = 0.1, hjust = 0, gp=gpar(fontface="bold"))),
# row 2
arrangeGrob(
std_mntd_analysis_geo_plot + theme_no_legend,
left = "MNTD"
),
arrangeGrob(
std_mntd_analysis_pred_plot + theme_no_legend + remove_x_scale + ylab('')
),
# row 3
arrangeGrob(std_gape_fdiv_analysis_geo_plot + theme_no_legend, left = "Beak Width"),
arrangeGrob(
std_gape_fdiv_analysis_pred_plot + theme_no_legend + remove_x_scale + ylab('')
),
# row 4
arrangeGrob(std_hwi_fdiv_analysis_geo_plot + theme_no_legend, left = "HWI"),
arrangeGrob(
std_hwi_fdiv_analysis_pred_plot + theme_no_legend + remove_x_scale + ylab('')
),
# row 5
arrangeGrob(std_mass_fdiv_analysis_geo_plot + theme_no_legend, left = "Mass"),
arrangeGrob(
std_mass_fdiv_analysis_pred_plot + theme_no_legend + remove_x_scale + ylab('')
),
# row 6 - legends
arrangeGrob(geo_fig_legend),
arrangeGrob(pred_fig_legend),
heights = c(0.5, 2, 2, 2, 2, 1.25),
nrow = 6
)
jpeg(filename(FIGURES_OUTPUT_DIR, 'figure2.jpg'), width = 183, height = 180, units = 'mm', res = 450)
grid.arrange(
arrangeGrob(fig2),
ncol = 1
)
dev.off()null device
1 pdf(filename(FIGURES_OUTPUT_DIR, 'figure2.pdf'), width = 12, height = 12, family = "Helvetica")
grid.arrange(
arrangeGrob(fig2),
ncol = 1
)
dev.off()null device
1 Compare to spatial model
plot_grid(
plot_grid(
ggdraw() +
draw_label(
"Spatial Model",
fontface = 'bold',
x = 0,
hjust = 0
),
ggdraw() +
draw_label(
"Dredge Result",
fontface = 'bold',
x = 0,
hjust = 0
)
),
plot_grid(
std_mntd_analysis_pred_spatial_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''),
std_mntd_analysis_pred_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''),
nrow = 1
) + draw_label("MNTD", size = 16, angle = 90, x = 0.01, y = 0.5),
plot_grid(
std_gape_fdiv_analysis_pred_spatial_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''),
std_gape_fdiv_analysis_pred_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''),
nrow = 1
) + draw_label("Beak Width", size = 16, angle = 90, x = 0.01, y = 0.5),
plot_grid(
std_hwi_fdiv_analysis_pred_spatial_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''),
std_hwi_fdiv_analysis_pred_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''),
nrow = 1
) + draw_label("HWI", size = 16, angle = 90, x = 0.01, y = 0.5),
plot_grid(
std_mass_fdiv_analysis_pred_spatial_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''),
std_mass_fdiv_analysis_pred_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''),
nrow = 1
) + draw_label("Mass", size = 16, angle = 90, x = 0.01, y = 0.5),
nrow = 5
)
ggsave(filename(FIGURES_OUTPUT_DIR, 'process_response_vs_spatial.jpg'), width = 3000, height = 3200, units = 'px')
Compare metrics against each other
ggplot(analysis_data, aes(x = beak_width_fdiv_standard, y = mntd_standard, colour = core_realm)) +
geom_point() +
ylab("MNTD") +
xlab("Beak Width FDiv") +
theme_bw() + labs(color = "Realm")
ggplot(analysis_data, aes(x = hwi_fdiv_standard, y = mntd_standard, colour = core_realm)) +
geom_point() +
ylab("MNTD") +
xlab("HWI FDiv") +
theme_bw() + labs(color = "Realm")
ggplot(analysis_data, aes(x = hwi_fdiv_standard, y = beak_width_fdiv_standard, colour = core_realm)) +
geom_point() +
ylab("Beak Width FDiv") +
xlab("HWI FDiv") +
theme_bw() + labs(color = "Realm")
mntd_fdiv_analysis = analysis_data %>%
dplyr::select(city_id, mntd_standard, hwi_fdiv_standard, beak_width_fdiv_standard, mass_fdiv_standard) %>%
left_join(community_summary) %>%
mutate(urban_pool_perc = urban_pool_size * 100 / regional_pool_size)
mntd_fdiv_analysisggpairs(mntd_fdiv_analysis %>% dplyr::select(mntd_standard, hwi_fdiv_standard, beak_width_fdiv_standard, mass_fdiv_standard, regional_pool_size, urban_pool_size, urban_pool_perc), columnLabels = c('MNTD', 'HWI FD', 'Bk FD', 'Mss FD', 'Region Rich.', 'Urban Rich.', '% Urban'))
ggsave(filename(FIGURES_OUTPUT_DIR, 'appendix_standarised_correlation.jpg'))
---
title: "Metrics for assessing community assembly processes"
output: html_notebook
bibliography: ../ref.bib  
---

```{r}
source('../env.R')
```

```{r}
community_data = read_csv(filename(COMMUNITY_OUTPUT_DIR, 'community_assembly_metrics_using_relative_abundance.csv'))
head(community_data)
colnames(community_data)
```

```{r}
min(community_data$mntd_standard)
max(community_data$mntd_standard)
min(community_data$beak_width_fdiv_standard)
max(community_data$beak_width_fdiv_standard)
min(community_data$hwi_fdiv_standard)
max(community_data$hwi_fdiv_standard)
min(community_data$mass_fdiv_standard)
max(community_data$mass_fdiv_standard)
```

```{r}
min(community_data$mntd_normal)
max(community_data$mntd_normal)
min(community_data$beak_width_fdiv_normal)
max(community_data$beak_width_fdiv_normal)
min(community_data$hwi_fdiv_normal)
max(community_data$hwi_fdiv_normal)
min(community_data$mass_fdiv_normal)
max(community_data$mass_fdiv_normal)
```

Join on realms
```{r}
city_to_realm = read_csv(filename(CITY_DATA_OUTPUT_DIR, 'realms.csv'))
community_data_with_realm = left_join(community_data, city_to_realm)
```

Cities as points
```{r}
city_points = st_centroid(read_sf(filename(CITY_DATA_OUTPUT_DIR, 'city_selection.shp'))) %>% left_join(community_data_with_realm)
city_points_coords = st_coordinates(city_points)
city_points$latitude = city_points_coords[,1]
city_points$longitude = city_points_coords[,2]
```
  
```{r}
world_map = read_country_boundaries()
```

Load community data, and create long format version
```{r}
communities = read_csv(filename(COMMUNITY_OUTPUT_DIR, 'communities_for_analysis.csv'))
communities
```

```{r}
community_summary = communities %>% group_by(city_id) %>% summarise(regional_pool_size = n(), urban_pool_size = sum(relative_abundance_proxy > 0))
community_summary
```

Load trait data
```{r}
traits = read_csv(filename(TAXONOMY_OUTPUT_DIR, 'traits_ebird.csv'))
head(traits)
```

Load spatial var
```{r}
spatial_var = read_csv(filename(COMMUNITY_OUTPUT_DIR, 'spatial_var.csv')) %>% filter(city_id %in% community_summary$city_id)
spatial_var
```

# Summary metrics by Realm
```{r}
test_required_values = function(name, df) {
  cat(paste(
    test_value_wilcox(paste(name, 'Std: MNTD'), df$mntd_standard),
    test_value_wilcox(paste(name, 'Std: Beak Width FDiv'), df$beak_width_fdiv_standard),
    test_value_wilcox(paste(name, 'Std: HWI FDiv'), df$hwi_fdiv_standard),
    test_value_wilcox(paste(name, 'Std: Mass FDiv'), df$mass_fdiv_standard),
    
    test_value_wilcox(paste(name, 'Norm: MNTD'), df$mntd_normal, mu = 0.5),
    test_value_wilcox(paste(name, 'Norm: Beak Width FDiv'), df$beak_width_fdiv_normal, mu = 0.5),
    test_value_wilcox(paste(name, 'Norm: HWI FDiv'), df$hwi_fdiv_normal, mu = 0.5),
    test_value_wilcox(paste(name, 'Norm: Mass FDiv'), df$mass_fdiv_normal, mu = 0.5),
    paste('N', nrow(df)),
    sep = "\n"))
}
```

```{r}
test_required_values('Global', community_data_with_realm)
```

```{r}
unique(community_data_with_realm$core_realm)
```

```{r}
test_required_values('Nearctic', community_data_with_realm[community_data_with_realm$core_realm == 'Nearctic',])
```

```{r}
test_required_values('Neotropic', community_data_with_realm[community_data_with_realm$core_realm == 'Neotropic',])
```

```{r}
test_required_values('Palearctic', community_data_with_realm[community_data_with_realm$core_realm == 'Palearctic',])
```

```{r}
test_required_values('Afrotropic', community_data_with_realm[community_data_with_realm$core_realm == 'Afrotropic',])
```

```{r}
test_required_values('Indomalayan', community_data_with_realm[community_data_with_realm$core_realm == 'Indomalayan',])
```

```{r}
test_required_values('Australasia', community_data_with_realm[community_data_with_realm$core_realm == 'Australasia',])
```

## How significant are those differences:
```{r}
print('Standard')
kruskal.test(mntd_standard~core_realm, data = community_data_with_realm)
pairwise.wilcox.test(community_data_with_realm$mntd_standard, community_data_with_realm$core_realm)
print('Normal')
kruskal.test(mntd_normal~core_realm, data = community_data_with_realm)
pairwise.wilcox.test(community_data_with_realm$mntd_normal, community_data_with_realm$core_realm)
```

```{r}
print('Standard')
kruskal.test(beak_width_fdiv_standard~core_realm, data = community_data_with_realm)
pairwise.wilcox.test(community_data_with_realm$beak_width_fdiv_standard, community_data_with_realm$core_realm)
print('Normal')
kruskal.test(beak_width_fdiv_standard~core_realm, data = community_data_with_realm)
pairwise.wilcox.test(community_data_with_realm$beak_width_fdiv_standard, community_data_with_realm$core_realm)
```

```{r}
print('Standard')
kruskal.test(hwi_fdiv_standard~core_realm, data = community_data_with_realm)
pairwise.wilcox.test(community_data_with_realm$hwi_fdiv_standard, community_data_with_realm$core_realm)
print('Normal')
kruskal.test(hwi_fdiv_normal~core_realm, data = community_data_with_realm)
pairwise.wilcox.test(community_data_with_realm$hwi_fdiv_normal, community_data_with_realm$core_realm)
```

```{r}
print('Standard')
kruskal.test(mass_fdiv_standard~core_realm, data = community_data_with_realm)
pairwise.wilcox.test(community_data_with_realm$mass_fdiv_standard, community_data_with_realm$core_realm)
print('Normal')
kruskal.test(mass_fdiv_normal~core_realm, data = community_data_with_realm)
pairwise.wilcox.test(community_data_with_realm$mass_fdiv_normal, community_data_with_realm$core_realm)
```
# What families exist in which realms?
```{r}
communities %>% 
  left_join(city_to_realm) %>% 
  mutate(family = gsub( " .*$", "", ebird_species_name)) %>%
  dplyr::select(family, core_realm) %>%
  distinct() %>%
  arrange(core_realm)
```

## Total urban families
```{r}
communities %>% 
  mutate(family = gsub( " .*$", "", ebird_species_name)) %>%
  dplyr::select(family) %>%
  distinct() %>%
  arrange()
```

of which urban
```{r}
communities %>% 
  filter(relative_abundance_proxy > 0) %>%
  mutate(family = gsub( " .*$", "", ebird_species_name)) %>%
  dplyr::select(family) %>%
  distinct() %>%
  arrange()
```

# Summary metrics by introduced species
```{r}
communities = read_csv(filename(COMMUNITY_OUTPUT_DIR, 'communities_for_analysis.csv'))
city_introduced_species = communities %>% group_by(city_id) %>% summarise(number_of_species = n()) %>% left_join(
  communities %>% group_by(city_id) %>% filter(origin == 'Introduced') %>% summarise(number_of_introduced_species = n())
) %>% replace_na(list(number_of_introduced_species = 0))

community_data_with_introductions = left_join(community_data, city_introduced_species)
community_data_with_introductions$has_introduced_species = community_data_with_introductions$number_of_introduced_species > 0
community_data_with_introductions
```

```{r}
communities %>% 
  filter(origin == 'Introduced') %>%
  dplyr::select(ebird_species_name) %>%
  group_by(ebird_species_name) %>%
  summarise(total_cities = n()) %>%
  arrange(desc(total_cities))
```

```{r}
community_data_with_introductions[,c('mntd_standard', 'has_introduced_species')]
```

```{r}
community_data_with_introductions %>% group_by(has_introduced_species) %>% summarise(
  total_cities = n(), 
  
  mean_mntd_std = mean(mntd_standard, na.rm = T),
  median_mntd_std = median(mntd_standard, na.rm = T),
  sd_mntd_std = sd(mntd_standard, na.rm = T),
  
  mean_mass_fdiv_std = mean(mass_fdiv_standard, na.rm = T),
  median_mass_fdiv_std = median(mass_fdiv_standard, na.rm = T),
  sd_mass_fdiv_std = sd(mass_fdiv_standard, na.rm = T),
  
  mean_gape_width_fdiv_std = mean(beak_width_fdiv_standard, na.rm = T),
  median_gape_width_fdiv_std = median(beak_width_fdiv_standard, na.rm = T),
  sd_gape_width_fdiv_std = sd(beak_width_fdiv_standard, na.rm = T),
  
  mean_handwing_index_fdiv_std = mean(hwi_fdiv_standard, na.rm = T),
  median_handwing_index_fdiv_std = median(hwi_fdiv_standard, na.rm = T),
  sd_handwing_index_fdiv_std = sd(hwi_fdiv_standard, na.rm = T)
)
```

## MNTD
```{r}
ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = mntd_standard)) + geom_boxplot()
```
```{r}
ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = mntd_normal)) + geom_boxplot()
```

```{r}
wilcox.test(mntd_standard ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')
```

There is a significant difference between the response of cities with introduced species (0.53±0.27) and those without (0.47±0.19) (p-value = 0.02).


```{r}
wilcox.test(mntd_normal ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')
```

## Mass FDiv
```{r}
ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = mass_fdiv_standard)) + geom_boxplot()
```

```{r}
ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = mass_fdiv_normal)) + geom_boxplot()
```
```{r}
wilcox.test(mass_fdiv_standard ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')
```
There is a significant difference between the response of cities with introduced species (0.57±0.27) and those without (0.73±0.24) (p < 0.0001)

```{r}
wilcox.test(mass_fdiv_normal ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')
```

## Beak Gape FDiv
```{r}
ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = beak_width_fdiv_standard)) + geom_boxplot()
```

```{r}
ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = beak_width_fdiv_normal)) + geom_boxplot()
```
```{r}
wilcox.test(beak_width_fdiv_standard ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')
```
There is NOT a significant difference between the response of cities with introduced species (0.61±0.30) and those without (0.56±0.27)

```{r}
wilcox.test(beak_width_fdiv_normal ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')
```

## HWI FDiv
```{r}
ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = hwi_fdiv_standard)) + geom_boxplot()
```

```{r}
ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = hwi_fdiv_normal)) + geom_boxplot()
```

```{r}
wilcox.test(hwi_fdiv_standard ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')
```
There is a significant difference between the response of cities with introduced species (0.49±0.30) and those without (0.79±0.21) (p < 0.0001)

```{r}
wilcox.test(hwi_fdiv_normal ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')
```

## What proportion of cities in each realm have introduced species
```{r}
community_data_with_introductions %>% left_join(city_to_realm) %>%
  group_by(core_realm) %>%
  summarise(
    total_cities = n(), 
    total_cities_with_introduced = sum(has_introduced_species), 
    total_cities_without_introduced = n() - sum(has_introduced_species)) %>%
  arrange(core_realm)
```

## Are any introduced species not also present in a city?
```{r}
communities %>% 
  filter(origin == 'Introduced') %>% 
  filter(relative_abundance_proxy < 0.1)
```

## What's the average relative abundance of introduced species compared to native
```{r}
communities %>% 
  group_by(origin) %>% 
  summarise(average_relative_abundance = mean(relative_abundance_proxy))
```

```{r}
communities %>% 
  group_by(origin) %>% 
  filter(relative_abundance_proxy > 0) %>%
  summarise(average_relative_abundance = mean(relative_abundance_proxy))
```

```{r}
communities %>% 
  group_by(origin) %>% 
  summarise(average_relative_abundance = mean(relative_abundance_proxy))
```

# Create analysis data frame
```{r}
geography = read_csv(filename(CITY_DATA_OUTPUT_DIR, 'geography.csv'))
names(geography)
```

```{r}
analysis_data = community_data_with_realm[,c('city_id', 
       'mntd_standard', 'mass_fdiv_standard', 'beak_width_fdiv_standard', 'hwi_fdiv_standard',
       'mntd_normal', 'mass_fdiv_normal', 'beak_width_fdiv_normal', 'hwi_fdiv_normal',
       'core_realm')] %>% 
  left_join(city_points[,c('city_id', 'latitude', 'longitude')]) %>%
  left_join(community_data_with_introductions[,c('city_id', 'has_introduced_species')]) %>%
  left_join(geography) %>%
  left_join(spatial_var)

analysis_data$abs_latitude = abs(analysis_data$latitude)
analysis_data$core_realm = factor(analysis_data$core_realm, levels = c('Palearctic', 'Nearctic', 'Neotropic', 'Afrotropic', 'Indomalayan', 'Australasia', 'Oceania'))
analysis_data$has_introduced_species = factor(analysis_data$has_introduced_species, level = c('FALSE', 'TRUE'), labels = c('No introduced species', 'Introduced species'))
```

```{r}
model_data = function(df, dependant_var) {
  df[,c(dependant_var, 'core_realm', 'abs_latitude', 'longitude', 'has_introduced_species', 'city_avg_ndvi', 'city_avg_elevation', 'city_avg_temp', 'city_avg_min_monthly_temp', 'city_avg_max_monthly_temp', 'city_avg_monthly_temp', 'city_avg_rainfall', 'city_avg_max_monthly_rainfall', 'city_avg_min_monthly_rainfall', 'city_avg_soil_moisture', 'city_max_elev', 'city_min_elev', 'city_elev_range', 'region_20km_avg_ndvi', 'region_20km_avg_elevation', 'region_20km_avg_soil_moisture', 'region_20km_max_elev', 'region_20km_min_elev', 'region_20km_elev_range', 'region_50km_avg_ndvi', 'region_50km_avg_elevation', 'region_50km_avg_soil_moisture', 'region_50km_max_elev', 'region_50km_min_elev', 'region_50km_elev_range')]
}
model_data(analysis_data, 'mntd_standard')
model_data(analysis_data, 'mntd_normal')
```

```{r}
names(analysis_data)
```

## NMDS Spatial Helpers
```{r}
analysis_data_nmds_coords = analysis_data[,c('NMDS1', 'NMDS2')]
coordinates(analysis_data_nmds_coords)  = ~ NMDS1 + NMDS2

analysis_data_nmds_nearneigh <- knearneigh(analysis_data_nmds_coords)
analysis_data_nmds_neighbours <- knn2nb(analysis_data_nmds_nearneigh)
```

### Polygons around realms in NMDS plot
```{r}
cities_to_realms_nmds = read_csv(filename(CITY_DATA_OUTPUT_DIR, 'realms.csv')) %>% left_join(analysis_data) %>% filter(!is.na(NMDS1))
unique(cities_to_realms_nmds$core_realm)
realm_nmds_neartic_polygon = cities_to_realms_nmds %>% filter(core_realm == 'Nearctic') %>% slice(chull(NMDS1, NMDS2))
realm_nmds_neotropic_polygon = cities_to_realms_nmds %>% filter(core_realm == 'Neotropic') %>% slice(chull(NMDS1, NMDS2))
realm_nmds_palearctic_polygon = cities_to_realms_nmds %>% filter(core_realm == 'Palearctic') %>% slice(chull(NMDS1, NMDS2))
realm_nmds_afrotropic_polygon = cities_to_realms_nmds %>% filter(core_realm == 'Afrotropic') %>% slice(chull(NMDS1, NMDS2))
realm_nmds_indomalayan_polygon = cities_to_realms_nmds %>% filter(core_realm == 'Indomalayan') %>% slice(chull(NMDS1, NMDS2))
realm_nmds_australasia_polygon = cities_to_realms_nmds %>% filter(core_realm == 'Australasia') %>% slice(chull(NMDS1, NMDS2))

polygon_line_type = 'dashed'
polygon_linewidth = 0.4

with_realms_nmds = function(g) {
  g + 
    geom_polygon(data = realm_nmds_neartic_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
    geom_polygon(data = realm_nmds_neotropic_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
    geom_polygon(data = realm_nmds_palearctic_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
    geom_polygon(data = realm_nmds_afrotropic_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
    geom_polygon(data = realm_nmds_indomalayan_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
    geom_polygon(data = realm_nmds_australasia_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0)
}
```

## Lat Long Spatial Helpers
```{r}
analysis_data_latlong_coords = analysis_data[,c('longitude', 'latitude')]
coordinates(analysis_data_latlong_coords)  = ~ longitude + latitude

analysis_data_coords_nearneigh <- knearneigh(analysis_data_latlong_coords, longlat = TRUE)
analysis_data_neighbours <- knn2nb(analysis_data_coords_nearneigh)
```

### Polygons around realms in lat long plot
```{r}
cities_to_realms_latlong = read_csv(filename(CITY_DATA_OUTPUT_DIR, 'realms.csv')) %>% left_join(analysis_data) %>% filter(!is.na(latitude))
unique(cities_to_realms_latlong$core_realm)
realm_latlong_neartic_polygon = cities_to_realms_latlong %>% filter(core_realm == 'Nearctic') %>% slice(chull(latitude, longitude))
realm_latlong_neotropic_polygon = cities_to_realms_latlong %>% filter(core_realm == 'Neotropic') %>% slice(chull(latitude, longitude))
realm_latlong_palearctic_polygon = cities_to_realms_latlong %>% filter(core_realm == 'Palearctic') %>% slice(chull(latitude, longitude))
realm_latlong_afrotropic_polygon = cities_to_realms_latlong %>% filter(core_realm == 'Afrotropic') %>% slice(chull(latitude, longitude))
realm_latlong_indomalayan_polygon = cities_to_realms_latlong %>% filter(core_realm == 'Indomalayan') %>% slice(chull(latitude, longitude))
realm_latlong_australasia_polygon = cities_to_realms_latlong %>% filter(core_realm == 'Australasia') %>% slice(chull(latitude, longitude))

with_realms_latlong = function(g) {
  g + 
    geom_polygon(data = realm_latlong_neartic_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
    geom_polygon(data = realm_latlong_neotropic_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
    geom_polygon(data = realm_latlong_palearctic_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
    geom_polygon(data = realm_latlong_afrotropic_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
    geom_polygon(data = realm_latlong_indomalayan_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0) +
    geom_polygon(data = realm_latlong_australasia_polygon, linewidth = polygon_linewidth, linetype = polygon_line_type, alpha = 0)
}
```

# Check for spatial autocorrelation

## MNTD

### Lat/Long
```{r}
with_realms_latlong(ggplot(analysis_data, aes(x = latitude, y = longitude, colour = mntd_standard)) + geom_point() + standardised_colours_scale + labs(colour = "Standardised response"))
```

```{r}
moran.test(analysis_data$mntd_standard, nb2listw(analysis_data_neighbours))
```
```{r}
with_realms_latlong(ggplot(analysis_data, aes(x = latitude, y = longitude, colour = mntd_normal)) + geom_point() + normalised_colours_scale + labs(colour = "Normalised response"))
```

```{r}
moran.test(analysis_data$mntd_normal, nb2listw(analysis_data_neighbours))
```

### NMDS
```{r}
with_realms_nmds(ggplot(analysis_data, aes(x = NMDS1, y = NMDS2, colour = mntd_standard)) + geom_point() + standardised_colours_scale + labs(colour = "Standardised response"))
```

```{r}
moran.test(analysis_data$mntd_standard, nb2listw(analysis_data_nmds_neighbours))
```

```{r}
with_realms_nmds(ggplot(analysis_data, aes(x = NMDS1, y = NMDS2, colour = mntd_normal)) + geom_point() + standardised_colours_scale + labs(colour = "Normalised response"))
```

```{r}
moran.test(analysis_data$mntd_normal, nb2listw(analysis_data_nmds_neighbours))
```

## FDiv Beak Width

### Lat/Long
```{r}
with_realms_latlong(ggplot(analysis_data, aes(x = latitude, y = longitude, colour = beak_width_fdiv_standard)) + geom_point() + standardised_colours_scale + labs(colour = "Standardised response"))
```

```{r}
moran.test(analysis_data$beak_width_fdiv_standard, nb2listw(analysis_data_neighbours))
```

```{r}
with_realms_latlong(ggplot(analysis_data, aes(x = latitude, y = longitude, colour = beak_width_fdiv_normal)) + geom_point() + standardised_colours_scale + labs(colour = "Normalised response"))
```

```{r}
moran.test(analysis_data$beak_width_fdiv_normal, nb2listw(analysis_data_neighbours))
```

### NMDS
```{r}
with_realms_nmds(ggplot(analysis_data, aes(x = NMDS1, y = NMDS2, colour = beak_width_fdiv_standard)) + geom_point() + standardised_colours_scale + labs(colour = "Standardised response"))
```

```{r}
moran.test(analysis_data$beak_width_fdiv_standard, nb2listw(analysis_data_nmds_neighbours))
```

```{r}
with_realms_nmds(ggplot(analysis_data, aes(x = NMDS1, y = NMDS2, colour = beak_width_fdiv_normal)) + geom_point() + standardised_colours_scale + labs(colour = "Normalised response"))
```

```{r}
moran.test(analysis_data$beak_width_fdiv_normal, nb2listw(analysis_data_nmds_neighbours))
```

## FDiv HWI

### Lat/Long
```{r}
with_realms_latlong(ggplot(analysis_data, aes(x = latitude, y = longitude, colour = hwi_fdiv_standard)) + geom_point() + standardised_colours_scale + labs(colour = "Standardised response"))
```

```{r}
moran.test(analysis_data$hwi_fdiv_standard, nb2listw(analysis_data_neighbours))
```

```{r}
with_realms_latlong(ggplot(analysis_data, aes(x = latitude, y = longitude, colour = hwi_fdiv_normal)) + geom_point() + standardised_colours_scale + labs(colour = "Normalised response"))
```

```{r}
moran.test(analysis_data$hwi_fdiv_normal, nb2listw(analysis_data_neighbours))
```

### NMDS
```{r}
with_realms_nmds(ggplot(analysis_data, aes(x = NMDS1, y = NMDS2, colour = hwi_fdiv_standard)) + geom_point() + standardised_colours_scale + labs(colour = "Standardised response"))
```

```{r}
moran.test(analysis_data$hwi_fdiv_standard, nb2listw(analysis_data_nmds_neighbours))
```

```{r}
with_realms_nmds(ggplot(analysis_data, aes(x = NMDS1, y = NMDS2, colour = hwi_fdiv_normal)) + geom_point() + standardised_colours_scale + labs(colour = "Normalised response"))
```

```{r}
moran.test(analysis_data$hwi_fdiv_normal, nb2listw(analysis_data_nmds_neighbours))
```

## FDiv Mass

### Lat/Long
```{r}
with_realms_latlong(ggplot(analysis_data, aes(x = latitude, y = longitude, colour = mass_fdiv_standard)) + geom_point() + standardised_colours_scale + labs(colour = "Standardised response"))
```

```{r}
moran.test(analysis_data$mass_fdiv_standard, nb2listw(analysis_data_neighbours))
```

```{r}
with_realms_latlong(ggplot(analysis_data, aes(x = latitude, y = longitude, colour = mass_fdiv_normal)) + geom_point() + standardised_colours_scale + labs(colour = "Normalised response"))
```

```{r}
moran.test(analysis_data$mass_fdiv_normal, nb2listw(analysis_data_neighbours))
```
### NMDS
```{r}
with_realms_nmds(ggplot(analysis_data, aes(x = NMDS1, y = NMDS2, colour = mass_fdiv_standard)) + geom_point() + standardised_colours_scale + labs(colour = "Standardised response"))
```

```{r}
moran.test(analysis_data$mass_fdiv_standard, nb2listw(analysis_data_nmds_neighbours))
```

```{r}
with_realms_nmds(ggplot(analysis_data, aes(x = NMDS1, y = NMDS2, colour = mass_fdiv_normal)) + geom_point() + standardised_colours_scale + labs(colour = "Normalised response"))
```

```{r}
moran.test(analysis_data$mass_fdiv_normal, nb2listw(analysis_data_nmds_neighbours))
```


# Examine individual metrics
```{r}
all_explanatories = c(
    'city_avg_ndvi', 'city_avg_elevation', 'city_avg_temp',
    'region_50km_avg_soil_moisture',
    'core_realmAfrotropic', 'core_realmAustralasia', 'core_realmIndomalayan', 'core_realmNearctic', 'core_realmNeotropic', 'core_realmPalearctic',
    'has_introduced_speciesNo introduced species', 'has_introduced_speciesIntroduced species'
)

all_explanatory_names = factor(
   c(
    'Avg. NDVI', 'Avg. Elevation', 'Avg. Temp.',
    'Avg. Soil Moisture',
    'Afrotropic', 'Australasia', 'Indomalayan', 'Nearctic', 'Neotropic', 'Palearctic',
    'Introduced Absent', 'Introduced Present'
  ), ordered = T
)

explanatory_dictionary = data.frame(explanatory = all_explanatories, explanatory_name = all_explanatory_names)
  
with_explanatory_type_labels = function(p) {
  p = p[p$explanatory != '(Intercept)',]
  explanatory_levels = all_explanatories[all_explanatories %in% p$explanatory]
  p$explanatory <- factor(p$explanatory, levels = explanatory_levels)
  
  p$type <- 'Realm'
  p$type[p$explanatory %in% c('city_avg_ndvi', 'city_avg_elevation', 'city_avg_temp')] <- 'City geography'
  p$type[p$explanatory %in% c('region_50km_avg_soil_moisture')] <- 'Regional (50 km) geography'
  p$type[p$explanatory %in% c('has_introduced_speciesNo introduced species', 'has_introduced_speciesIntroduced species')] <- 'Introduced species'
  p
}

with_explanatory_names = function(p) {
  p %>% left_join(explanatory_dictionary) %>% arrange(desc(explanatory_name))
}

type_labels = function(p) {
  explanatory_levels = all_explanatories[all_explanatories %in% p$explanatory]
  p$explanatory <- factor(p$explanatory, levels = explanatory_levels)
  
  p$type <- 'Realm'
  p$type[p$explanatory %in% c('city_avg_ndvi', 'city_avg_elevation', 'city_avg_temp', 'city_avg_min_monthly_temp', 'city_avg_max_monthly_temp', 
    'city_avg_monthly_temp', 'city_avg_rainfall', 'city_avg_max_monthly_rainfall', 'city_avg_min_monthly_rainfall', 
    'city_avg_soil_moisture', 'city_max_elev', 'city_min_elev', 'city_elev_range')] <- 'City geography'
  p$type[p$explanatory %in% c('region_50km_avg_ndvi', 'region_50km_avg_elevation', 'region_50km_avg_soil_moisture', 'region_50km_max_elev', 
    'region_50km_min_elev', 'region_50km_elev_range')] <- 'Regional (50 km) geography'
   p$type[p$explanatory %in% c('region_20km_avg_ndvi', 'region_20km_avg_elevation', 'region_20km_avg_soil_moisture', 'region_20km_max_elev', 
    'region_20km_min_elev', 'region_20km_elev_range')] <- 'Regional (20 km) geography'
  p$type[p$explanatory %in% c('has_introduced_speciesNo introduced species', 'has_introduced_speciesIntroduced species')] <- 'Introduced species'
  p
}
```

```{r}
explanatory_labels = c(
  'has_introduced_species'='Introduced species', 
  'has_introduced_speciesNo introduced species'='Introduced absent', 
  'has_introduced_speciesIntroduced species'='Introduced present',
  'city_avg_ndvi'='Average NDVI', 
  'city_avg_elevation'='Average elevation', 
  'city_avg_temp'='Average temperature', 
  'city_avg_min_monthly_temp'='Average minimum monthly temperature', 
  'city_avg_max_monthly_temp'='Average maximum monthly temperature', 
  'city_avg_monthly_temp'='Average monthly temperature', 
  'city_avg_rainfall'='Average rainfall', 
  'city_avg_max_monthly_rainfall'='Average maximum monthly rainfall', 
  'city_avg_min_monthly_rainfall'='Average minimum monthly rainfall', 
  'city_avg_soil_moisture'='Average soil moisture', 
  'city_max_elev'='Maximum elevation', 
  'city_min_elev'='Minimum elevation', 
  'city_elev_range'='Elevation range', 
  'region_20km_avg_ndvi'='Average NDVI', 
  'region_20km_avg_elevation'='Average elevation', 
  'region_20km_avg_soil_moisture'='Average soil moisture', 
  'region_20km_max_elev'='Maximum elevation', 
  'region_20km_min_elev'='Minimum elevation',
  'region_20km_elev_range'='Elevation range',
  'region_50km_avg_ndvi'='Average NDVI',
  'region_50km_avg_elevation'='Average elevation',
  'region_50km_avg_soil_moisture'='Average soil moisture', 
  'region_50km_max_elev'='Maximum elevation',
  'region_50km_min_elev'='Minimum elevation', 
  'region_50km_elev_range'='Elevation range',
  'abs_latitude' = 'Absolute latitude',
  'latitude' = 'Latitude',
  'longitude' = 'Longitude',
  'core_realmAfrotropic' = 'Afrotropical', 
  'core_realmAustralasia' = 'Austaliasian', 
  'core_realmIndomalayan' = 'Indomalayan', 
  'core_realmNearctic' = 'Nearctic', 
  'core_realmNeotropic' = 'Neotropical',
  'core_realmPalearctic' = 'Palearctic',
  'core_realmOceania' = 'Oceanical')
```

```{r}
create_formula = function(response_var) {
  as.formula(paste(response_var, '~ core_realm + city_avg_ndvi + city_avg_elevation + city_avg_temp + region_50km_avg_soil_moisture + has_introduced_species'))
}
```

## Helper plot functions
```{r}
geom_map = function(map_sf, title, scale = standardised_colours_scale, colour_label = 'Standardised\nResponse') {
  norm_mntd_analysis_geo = ggplot() + 
    geom_sf(data = world_map, aes(geometry = geometry)) +
    map_sf +
    scale +
    labs(colour = colour_label) +
    theme_bw() +
    theme(legend.position="bottom")
}

geom_map_std = function(map_sf, title) {
  geom_map(map_sf, title)
}

geom_map_nrm = function(map_sf, title) {
  geom_map(map_sf, title, normalised_colours_scale, 'Normalised\nResponse')
}
```

## Helper Dredge functions
```{r}
# Taken from MuMIN package
# https://rdrr.io/cran/MuMIn/src/R/averaging.R
# https://rdrr.io/cran/MuMIn/src/R/model.avg.R

.coefarr.avg <-
  function(cfarr, weight, revised.var, full, alpha) {	
    weight <- weight / sum(weight)
    nCoef <- dim(cfarr)[3L]
    if(full) {
      nas <- is.na(cfarr[, 1L, ]) & is.na(cfarr[, 2L, ])
      cfarr[, 1L, ][nas] <- cfarr[, 2L, ][nas] <- 0
      #cfarr[, 1L:2L, ][is.na(cfarr[, 1L:2L, ])] <- 0
      if(!all(is.na(cfarr[, 3L, ])))
        cfarr[ ,3L, ][is.na(cfarr[ , 3L, ])] <- Inf
    }
    
    avgcoef <- array(dim = c(nCoef, 5L),
                     dimnames = list(dimnames(cfarr)[[3L]], c("Estimate",
                                                              "Std. Error", "Adjusted SE", "Lower CI", "Upper CI")))
    for(i in seq_len(nCoef))
      avgcoef[i, ] <- par.avg(cfarr[, 1L, i], cfarr[, 2L, i], weight,
                              df = cfarr[, 3L, i], alpha = alpha, revised.var = revised.var)
    
    avgcoef[is.nan(avgcoef)] <- NA
    return(avgcoef)
  }

.makecoefmat <- function(cf) {
  no.ase <- all(is.na(cf[, 3L]))
  z <- abs(cf[, 1L] / cf[, if(no.ase) 2L else 3L])
  pval <- 2 * pnorm(z, lower.tail = FALSE)
  cbind(cf[, if(no.ase) 1L:2L else 1L:3L, drop = FALSE],
        `z value` = z, `Pr(>|z|)` = zapsmall(pval))
}

# Generate model selections using lmer, dredge, and model.avg
# `forumla` : a two-sided linear formula object describing both the fixed-effects and random-effects part of the model
# `data` : the data frame containing the variables from the formula
# `aic_delta` : the AIC delta to use for selecting models in model average
model_average <- function(formula, data, aic_delta = 20) {
  model <- lm(
    formula,
    data=data
  )
  dredge_result <- dredge(model)
  summary(model.avg(dredge_result, subset = delta < aic_delta))
}

# Create a summary data frame containing the selected variables from a model
# `model_sum` : The model summary output from `model_average`
model_summary <- function(model_sum) {
  .column_name <- function(postfix) {
    postfix
  }
  
  # just return the estimate and p value
  weight <- model_sum$msTable[, 5L]
  
  coefmat.full <- as.data.frame(.makecoefmat(.coefarr.avg(model_sum$coefArray, weight,
                                                          attr(model_sum, "revised.var"), TRUE, 0.05)))
  
  coefmat.subset <-
    as.data.frame(.makecoefmat(.coefarr.avg(model_sum$coefArray, weight,
                                            attr(model_sum, "revised.var"), FALSE, 0.05)))
  
  
  coefmat.subset <- coefmat.subset[-c(1), c(1, 2, 5)]
  names(coefmat.subset) <- c(.column_name("estimate"), .column_name("error"), .column_name("p"))
  coefmat.subset <- tibble::rownames_to_column(coefmat.subset, "explanatory")
  coefmat.subset$model = 'subset'
  
  coefmat.full <- coefmat.full[-c(1), c(1, 2, 5)]
  names(coefmat.full) <- c(.column_name("estimate"), .column_name("error"), .column_name("p"))
  coefmat.full <- tibble::rownames_to_column(coefmat.full, "explanatory")
  coefmat.full$model = 'full'
  
  rbind(coefmat.full, coefmat.subset)
}
```

```{r}
plot_dredge_result = function(result_table, mu = 0) {
  p = result_table[result_table$model == 'full',]
  p = type_labels(p)

  ggplot(p, aes(y = explanatory, x = estimate, colour = type)) + 
    geom_line() +
    geom_point() +
    geom_errorbar(aes(xmin=estimate-error, xmax=estimate+error), width=.2,
                   position=position_dodge(0.05)) +
    scale_y_discrete(
      limits = rev(levels(p$explanatory)), 
      labels = explanatory_labels) +
    scale_colour_manual(
      values = c(realm_colour, city_geography_colour, regional_50km_geography_colour, regional_20km_geography_colour, introduced_species_colour), 
      breaks = c('Realm', 'City geography', 'Regional (50 km) geography', 'Regional (20 km) geography', 'Introduced species')) +
    theme_bw() +
    geom_vline(xintercept=mu, linetype="dotted") +
    guides(colour=guide_legend(title="Predictor type")) + xlab('Difference in response from 0\nhabitat filtering (< 0) and competitive interactions (> 0)\n± Standard Error') + ylab('Predictor') +
    theme(legend.justification = "top")
}
```

## GLS Spatial Helpers
```{r}
gls_method = "ML"

spatial_model = function(formula, correlation) {
  gls(
    formula, 
    data = analysis_data, 
    correlation = correlation, 
    method = gls_method
  )
}

plot_spatial_result = function(model_result) {
  model_summary = summary(model_result)
  result_table = as.data.frame(model_summary$tTable)
  result_table$explanatory = rownames(result_table)
  
  result_table = result_table %>% with_explanatory_type_labels() %>% with_explanatory_names()
  
  ggplot2::ggplot(result_table, ggplot2::aes(y=factor(explanatory_name, level = all_explanatory_names, ordered = T), x=Value, colour = type)) + 
    ggplot2::geom_line() +
    ggplot2::geom_point() +
    ggplot2::geom_errorbar(ggplot2::aes(xmin=Value-Std.Error, xmax=Value+Std.Error), width=.2,
                   position=ggplot2::position_dodge(0.05)) +
    ggplot2::theme_bw() +
    ggplot2::geom_vline(xintercept=0, linetype="dotted") +
    ggplot2::theme(legend.justification = "top") +
    ylab('Predictor') +
    guides(colour=guide_legend(title="Predictor type")) + xlab('Difference in response from 0\nhabitat filtering (< 0) and competitive interactions (> 0)\n± Standard Error') +
    scale_colour_manual(
      values = c(realm_colour, city_geography_colour, regional_50km_geography_colour, introduced_species_colour), 
      breaks = c('Realm', 'City geography', 'Regional (50 km) geography', 'Introduced species')) +
    scale_y_discrete(limits = rev(all_explanatory_names[all_explanatory_names %in% result_table$explanatory_name]))
}
```

### Choose best spatial correlation function

#### MNTD
```{r}
AIC(spatial_model(create_formula('mntd_standard'), corLin(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('mntd_standard'), corLin(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('mntd_standard'), corExp(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('mntd_standard'), corExp(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('mntd_standard'), corGaus(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('mntd_standard'), corGaus(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('mntd_standard'), corRatio(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('mntd_standard'), corRatio(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('mntd_standard'), corSpher(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('mntd_standard'), corSpher(form = ~ latitude + longitude)))
```
MNTD: corRatio with NMDS + lat/long

```{r}
AIC(spatial_model(create_formula('mntd_normal'), corLin(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('mntd_normal'), corLin(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('mntd_normal'), corExp(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('mntd_normal'), corExp(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('mntd_normal'), corGaus(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('mntd_normal'), corGaus(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('mntd_normal'), corRatio(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('mntd_normal'), corRatio(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('mntd_normal'), corSpher(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('mntd_normal'), corSpher(form = ~ latitude + longitude)))
```

#### Beak Width

```{r}
AIC(spatial_model(create_formula('beak_width_fdiv_standard'), corLin(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
#AIC(spatial_model(create_formula('beak_width_fdiv_standard'), corLin(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('beak_width_fdiv_standard'), corExp(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('beak_width_fdiv_standard'), corExp(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('beak_width_fdiv_standard'), corGaus(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('beak_width_fdiv_standard'), corGaus(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('beak_width_fdiv_standard'), corRatio(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('beak_width_fdiv_standard'), corRatio(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('beak_width_fdiv_standard'), corSpher(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('beak_width_fdiv_standard'), corSpher(form = ~ latitude + longitude)))
```
Beak width: corExp with NMDS + lat/long

```{r}
AIC(spatial_model(create_formula('beak_width_fdiv_normal'), corLin(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('beak_width_fdiv_normal'), corLin(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('beak_width_fdiv_normal'), corExp(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('beak_width_fdiv_normal'), corExp(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('beak_width_fdiv_normal'), corGaus(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('beak_width_fdiv_normal'), corGaus(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('beak_width_fdiv_normal'), corRatio(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('beak_width_fdiv_normal'), corRatio(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('beak_width_fdiv_normal'), corSpher(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('beak_width_fdiv_normal'), corSpher(form = ~ latitude + longitude)))
```

#### HWI
```{r}
AIC(spatial_model(create_formula('hwi_fdiv_standard'), corLin(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('hwi_fdiv_standard'), corLin(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('hwi_fdiv_standard'), corExp(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('hwi_fdiv_standard'), corExp(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('hwi_fdiv_standard'), corGaus(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('hwi_fdiv_standard'), corGaus(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('hwi_fdiv_standard'), corRatio(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('hwi_fdiv_standard'), corRatio(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('hwi_fdiv_standard'), corSpher(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('hwi_fdiv_standard'), corSpher(form = ~ latitude + longitude)))
```

HWI: corExp with NMDS + lat/long

```{r}
#AIC(spatial_model(create_formula('hwi_fdiv_normal'), corLin(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
#AIC(spatial_model(create_formula('hwi_fdiv_normal'), corLin(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('hwi_fdiv_normal'), corExp(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('hwi_fdiv_normal'), corExp(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('hwi_fdiv_normal'), corGaus(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('hwi_fdiv_normal'), corGaus(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('hwi_fdiv_normal'), corRatio(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('hwi_fdiv_normal'), corRatio(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('hwi_fdiv_normal'), corSpher(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('hwi_fdiv_normal'), corSpher(form = ~ latitude + longitude)))
```

#### Mass
```{r}
AIC(spatial_model(create_formula('mass_fdiv_standard'), corLin(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
#AIC(spatial_model(create_formula('mass_fdiv_standard'), corLin(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('mass_fdiv_standard'), corExp(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('mass_fdiv_standard'), corExp(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('mass_fdiv_standard'), corGaus(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('mass_fdiv_standard'), corGaus(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('mass_fdiv_standard'), corRatio(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('mass_fdiv_standard'), corRatio(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('mass_fdiv_standard'), corSpher(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('mass_fdiv_standard'), corSpher(form = ~ latitude + longitude)))
```

Mass: corExp with NMDS + lat/long

```{r}
AIC(spatial_model(create_formula('mass_fdiv_normal'), corLin(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
#AIC(spatial_model(create_formula('mass_fdiv_normal'), corLin(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('mass_fdiv_normal'), corExp(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('mass_fdiv_normal'), corExp(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('mass_fdiv_normal'), corGaus(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('mass_fdiv_normal'), corGaus(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('mass_fdiv_normal'), corRatio(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('mass_fdiv_normal'), corRatio(form = ~ latitude + longitude)))
AIC(spatial_model(create_formula('mass_fdiv_normal'), corSpher(form = ~ NMDS1 + NMDS2 + latitude + longitude)))
AIC(spatial_model(create_formula('mass_fdiv_normal'), corSpher(form = ~ latitude + longitude)))
```

```{r}
correlation_formula = as.formula('~ NMDS1 + NMDS2 + latitude + longitude')

correlation_function_fdiv = function() {
  corExp(form = correlation_formula)
}

correlation_function_mntd = function() {
  corRatio(form = correlation_formula)
}
```

## MNTD

### Standardised
```{r}
std_mntd_analysis_geo_plot = geom_map_std(geom_sf(data = analysis_data, aes(color = mntd_standard, geometry = geometry)), 'MNTD')
std_mntd_analysis_geo_plot
```

```{r}
std_mntd_analysis_data = model_data(analysis_data[!is.na(analysis_data$mntd_standard),], 'mntd_standard')
std_mntd_analysis_formula = create_formula('mntd_standard')
std_mntd_analysis_result = model_average(std_mntd_analysis_formula, std_mntd_analysis_data)
std_mntd_analysis_result_table = model_summary(std_mntd_analysis_result)
std_mntd_analysis_result_table
```

```{r}
std_mntd_analysis_pred_plot = plot_dredge_result(std_mntd_analysis_result_table)
std_mntd_analysis_pred_plot
```

Do the residuals still contain spatial autocorrelation from a fitted lm?
```{r}
std_mntd_lm = lm(std_mntd_analysis_formula, std_mntd_analysis_data)
moran.test(std_mntd_lm$residuals, nb2listw(analysis_data_neighbours))
moran.test(std_mntd_lm$residuals, nb2listw(analysis_data_nmds_neighbours))
```

```{r}
std_mntd_spatial_model = spatial_model(std_mntd_analysis_formula, correlation_function_mntd())
moran.test(std_mntd_spatial_model$residuals, nb2listw(analysis_data_neighbours))
moran.test(std_mntd_spatial_model$residuals, nb2listw(analysis_data_nmds_neighbours))
```

```{r}
std_mntd_analysis_pred_spatial_plot = plot_spatial_result(std_mntd_spatial_model)
std_mntd_analysis_pred_spatial_plot
```

### Normalised
```{r}
nrm_mntd_analysis_geo_plot = geom_map_nrm(geom_sf(data = analysis_data, aes(color = mntd_normal, geometry = geometry)), 'MNTD')
nrm_mntd_analysis_geo_plot
```

```{r}
nrm_mntd_analysis_data = model_data(analysis_data[!is.na(analysis_data$mntd_normal),], 'mntd_normal')
nrm_mntd_analysis_formula = create_formula('mntd_normal')
nrm_mntd_analysis_result = model_average(nrm_mntd_analysis_formula, nrm_mntd_analysis_data)
nrm_mntd_analysis_result_table = model_summary(nrm_mntd_analysis_result)
nrm_mntd_analysis_result_table
```

```{r}
nrm_mntd_analysis_pred_plot = plot_dredge_result(nrm_mntd_analysis_result_table)
nrm_mntd_analysis_pred_plot
```

Do the residuals still contain spatial autocorrelation from a fitted lm?
```{r}
nrm_mntd_lm = lm(nrm_mntd_analysis_formula, nrm_mntd_analysis_data)
moran.test(nrm_mntd_lm$residuals, nb2listw(analysis_data_neighbours))
moran.test(nrm_mntd_lm$residuals, nb2listw(analysis_data_nmds_neighbours))
```

```{r}
nrm_mntd_spatial_model = spatial_model(nrm_mntd_analysis_formula, correlation_function_mntd())
moran.test(nrm_mntd_spatial_model$residuals, nb2listw(analysis_data_neighbours))
moran.test(nrm_mntd_spatial_model$residuals, nb2listw(analysis_data_nmds_neighbours))
```

```{r}
nrm_mntd_analysis_pred_spatial_plot = plot_spatial_result(nrm_mntd_spatial_model)
nrm_mntd_analysis_pred_spatial_plot
```

## Gape width - FDiv
### Standardised
```{r}
std_gape_fdiv_analysis_geo_plot = geom_map_std(geom_sf(data = analysis_data, aes(color = beak_width_fdiv_standard, geometry = geometry)), 'Beak Width FDiv')
std_gape_fdiv_analysis_geo_plot
```


```{r}
std_gape_fdiv_analysis_data = model_data(analysis_data[!is.na(analysis_data$beak_width_fdiv_standard),], 'beak_width_fdiv_standard')
std_gape_fdiv_analysis_formula = create_formula('beak_width_fdiv_standard')
std_gape_fdiv_analysis_result = model_average(std_gape_fdiv_analysis_formula, std_gape_fdiv_analysis_data)
std_gape_fdiv_analysis_result_table = model_summary(std_gape_fdiv_analysis_result)
std_gape_fdiv_analysis_result_table
```

```{r}
std_gape_fdiv_analysis_pred_plot = plot_dredge_result(std_gape_fdiv_analysis_result_table)
std_gape_fdiv_analysis_pred_plot
```

Do the residuals still contain spatial autocorrelation from a fitted lm?
```{r}
std_gape_fdiv_lm = lm(std_gape_fdiv_analysis_formula, std_gape_fdiv_analysis_data)
moran.test(std_gape_fdiv_lm$residuals, nb2listw(analysis_data_neighbours))
moran.test(std_gape_fdiv_lm$residuals, nb2listw(analysis_data_nmds_neighbours))
```

```{r}
std_gape_fdiv_spatial_model = spatial_model(std_gape_fdiv_analysis_formula, correlation_function_fdiv())
moran.test(std_gape_fdiv_spatial_model$residuals, nb2listw(analysis_data_neighbours))
moran.test(std_gape_fdiv_spatial_model$residuals, nb2listw(analysis_data_nmds_neighbours))
```

```{r}
std_gape_fdiv_analysis_pred_spatial_plot = plot_spatial_result(std_gape_fdiv_spatial_model)
std_gape_fdiv_analysis_pred_spatial_plot
```

### Normalised
```{r}
nrm_gape_fdiv_analysis_geo_plot = geom_map_nrm(geom_sf(data = analysis_data, aes(color = beak_width_fdiv_normal, geometry = geometry)), 'Beak Width FDiv')
nrm_gape_fdiv_analysis_geo_plot
```


```{r}
nrm_gape_fdiv_analysis_data = model_data(analysis_data[!is.na(analysis_data$beak_width_fdiv_normal),], 'beak_width_fdiv_normal')
nrm_gape_fdiv_analysis_formula = create_formula('beak_width_fdiv_normal')
nrm_gape_fdiv_analysis_result = model_average(nrm_gape_fdiv_analysis_formula, nrm_gape_fdiv_analysis_data)
nrm_gape_fdiv_analysis_result_table = model_summary(nrm_gape_fdiv_analysis_result)
nrm_gape_fdiv_analysis_result_table
```

```{r}
nrm_gape_fdiv_analysis_pred_plot = plot_dredge_result(nrm_gape_fdiv_analysis_result_table)
nrm_gape_fdiv_analysis_pred_plot
```

Do the residuals still contain spatial autocorrelation from a fitted lm?
```{r}
nrm_gape_fdiv_lm = lm(nrm_gape_fdiv_analysis_formula, nrm_gape_fdiv_analysis_data)
moran.test(nrm_gape_fdiv_lm$residuals, nb2listw(analysis_data_neighbours))
moran.test(nrm_gape_fdiv_lm$residuals, nb2listw(analysis_data_nmds_neighbours))
```

```{r}
nrm_gape_fdiv_spatial_model = spatial_model(nrm_gape_fdiv_analysis_formula, correlation_function_fdiv())
moran.test(nrm_gape_fdiv_spatial_model$residuals, nb2listw(analysis_data_neighbours))
moran.test(nrm_gape_fdiv_spatial_model$residuals, nb2listw(analysis_data_nmds_neighbours))
```

```{r}
nrm_gape_fdiv_analysis_pred_spatial_plot = plot_spatial_result(nrm_gape_fdiv_spatial_model)
nrm_gape_fdiv_analysis_pred_spatial_plot
```

## HWI - FDiv
### Standardised
```{r}
std_hwi_fdiv_analysis_geo_plot = geom_map_std(geom_sf(data = analysis_data, aes(color = hwi_fdiv_standard, geometry = geometry)), 'HWI FDiv')
std_hwi_fdiv_analysis_geo_plot
```



```{r}
std_hwi_fdiv_analysis_data = model_data(analysis_data[!is.na(analysis_data$hwi_fdiv_standard),], 'hwi_fdiv_standard')
std_hwi_fdiv_analysis_formula = create_formula('hwi_fdiv_standard')
std_hwi_fdiv_analysis_result = model_average(std_hwi_fdiv_analysis_formula, std_hwi_fdiv_analysis_data)
std_hwi_fdiv_analysis_result_table = model_summary(std_hwi_fdiv_analysis_result)
std_hwi_fdiv_analysis_result_table
```

```{r}
std_hwi_fdiv_analysis_pred_plot = plot_dredge_result(std_hwi_fdiv_analysis_result_table)
std_hwi_fdiv_analysis_pred_plot
```

```{r}
std_hwi_fdiv_lm = lm(std_hwi_fdiv_analysis_formula, std_hwi_fdiv_analysis_data)
moran.test(std_hwi_fdiv_lm$residuals, nb2listw(analysis_data_neighbours))
moran.test(std_hwi_fdiv_lm$residuals, nb2listw(analysis_data_nmds_neighbours))
```

```{r}
std_hwi_fdiv_spatial_model = spatial_model(std_hwi_fdiv_analysis_formula, correlation_function_fdiv())
moran.test(std_hwi_fdiv_spatial_model$residuals, nb2listw(analysis_data_neighbours))
moran.test(std_hwi_fdiv_spatial_model$residuals, nb2listw(analysis_data_nmds_neighbours))
```

```{r}
std_hwi_fdiv_analysis_pred_spatial_plot = plot_spatial_result(std_hwi_fdiv_spatial_model)
std_hwi_fdiv_analysis_pred_spatial_plot
```

### Normalised
```{r}
nrm_hwi_fdiv_analysis_geo_plot = geom_map_nrm(geom_sf(data = analysis_data, aes(color = hwi_fdiv_normal, geometry = geometry)), 'HWI FDiv')
nrm_hwi_fdiv_analysis_geo_plot
```


```{r}
nrm_hwi_fdiv_analysis_data = model_data(analysis_data[!is.na(analysis_data$hwi_fdiv_normal),], 'hwi_fdiv_normal')
nrm_hwi_fdiv_analysis_formula = create_formula('hwi_fdiv_normal')
nrm_hwi_fdiv_analysis_result = model_average(nrm_hwi_fdiv_analysis_formula, nrm_hwi_fdiv_analysis_data)
nrm_hwi_fdiv_analysis_result_table = model_summary(nrm_hwi_fdiv_analysis_result)
nrm_hwi_fdiv_analysis_result_table
```

```{r}
nrm_hwi_fdiv_analysis_pred_plot = plot_dredge_result(nrm_hwi_fdiv_analysis_result_table)
nrm_hwi_fdiv_analysis_pred_plot
```

```{r}
nrm_hwi_fdiv_lm = lm(nrm_hwi_fdiv_analysis_formula, nrm_hwi_fdiv_analysis_data)
moran.test(nrm_hwi_fdiv_lm$residuals, nb2listw(analysis_data_neighbours))
moran.test(nrm_hwi_fdiv_lm$residuals, nb2listw(analysis_data_nmds_neighbours))
```

```{r}
nrm_hwi_fdiv_spatial_model = spatial_model(nrm_hwi_fdiv_analysis_formula, correlation_function_fdiv())
moran.test(nrm_hwi_fdiv_spatial_model$residuals, nb2listw(analysis_data_neighbours))
moran.test(nrm_hwi_fdiv_spatial_model$residuals, nb2listw(analysis_data_nmds_neighbours))
```

```{r}
nrm_hwi_fdiv_analysis_pred_spatial_plot = plot_spatial_result(nrm_hwi_fdiv_spatial_model)
nrm_hwi_fdiv_analysis_pred_spatial_plot
```

## Mass - FDiv
### Standardised
```{r}
std_mass_fdiv_analysis_geo_plot = geom_map_std(geom_sf(data = analysis_data, aes(color = mass_fdiv_standard, geometry = geometry)), 'Mass FDiv')
std_mass_fdiv_analysis_geo_plot
```


```{r}
std_mass_fdiv_analysis_data = model_data(analysis_data[!is.na(analysis_data$mass_fdiv_standard),], 'mass_fdiv_standard')
std_mass_fdiv_analysis_formula = create_formula('mass_fdiv_standard')
std_mass_fdiv_analysis_result <- model_average(std_mass_fdiv_analysis_formula, std_mass_fdiv_analysis_data)
std_mass_fdiv_analysis_result_table = model_summary(std_mass_fdiv_analysis_result)
std_mass_fdiv_analysis_result_table
```

```{r}
std_mass_fdiv_analysis_pred_plot = plot_dredge_result(std_mass_fdiv_analysis_result_table)
std_mass_fdiv_analysis_pred_plot
```

```{r}
std_mass_fdiv_lm = lm(std_mass_fdiv_analysis_formula, std_mass_fdiv_analysis_data)
moran.test(std_mass_fdiv_lm$residuals, nb2listw(analysis_data_neighbours))
moran.test(std_mass_fdiv_lm$residuals, nb2listw(analysis_data_nmds_neighbours))
```

```{r}
std_mass_fdiv_spatial_model = spatial_model(std_mass_fdiv_analysis_formula, correlation_function_fdiv())
moran.test(std_mass_fdiv_spatial_model$residuals, nb2listw(analysis_data_neighbours))
moran.test(std_mass_fdiv_spatial_model$residuals, nb2listw(analysis_data_nmds_neighbours))
```

```{r}
std_mass_fdiv_analysis_pred_spatial_plot = plot_spatial_result(std_mass_fdiv_spatial_model)
std_mass_fdiv_analysis_pred_spatial_plot
```


### Normalised
```{r}
nrm_mass_fdiv_analysis_geo_plot = geom_map_nrm(geom_sf(data = analysis_data, aes(color = mass_fdiv_normal, geometry = geometry)), 'Mass FDiv')
nrm_mass_fdiv_analysis_geo_plot
```


```{r}
nrm_mass_fdiv_analysis_data = model_data(analysis_data[!is.na(analysis_data$mass_fdiv_normal),], 'mass_fdiv_normal')
nrm_mass_fdiv_analysis_formula = create_formula('mass_fdiv_normal')
nrm_mass_fdiv_analysis_result = model_average(nrm_mass_fdiv_analysis_formula, nrm_mass_fdiv_analysis_data)
nrm_mass_fdiv_analysis_result_table = model_summary(nrm_mass_fdiv_analysis_result)
nrm_mass_fdiv_analysis_result_table
```

```{r}
nrm_mass_fdiv_analysis_pred_plot = plot_dredge_result(nrm_mass_fdiv_analysis_result_table)
nrm_mass_fdiv_analysis_pred_plot
```

```{r}
nrm_mass_fdiv_lm = lm(nrm_mass_fdiv_analysis_formula, nrm_mass_fdiv_analysis_data)
moran.test(nrm_mass_fdiv_lm$residuals, nb2listw(analysis_data_neighbours))
moran.test(nrm_mass_fdiv_lm$residuals, nb2listw(analysis_data_nmds_neighbours))
```

```{r}
nrm_mass_fdiv_spatial_model = spatial_model(nrm_mass_fdiv_analysis_formula, correlation_function_fdiv())
moran.test(nrm_mass_fdiv_spatial_model$residuals, nb2listw(analysis_data_neighbours))
moran.test(nrm_mass_fdiv_spatial_model$residuals, nb2listw(analysis_data_nmds_neighbours))
```

```{r}
nrm_mass_fdiv_analysis_pred_spatial_plot = plot_spatial_result(nrm_mass_fdiv_spatial_model)
nrm_mass_fdiv_analysis_pred_spatial_plot
```

# Create plot of differences in process response
```{r}
pred_legend <- ggpubr::get_legend(
  # create some space to the left of the legend
  std_hwi_fdiv_analysis_pred_plot + theme(legend.box.margin = margin(0, 0, 0, 0)) + guides(colour=guide_legend(ncol=2)) + labs(color = "Predictor type")
)
geo_legend <- ggpubr::get_legend(
  # create some space to the left of the legend
  std_mass_fdiv_analysis_geo_plot + theme(legend.box.margin = margin(-80, 0, 0, 12), legend.title.position = "top", legend.key.width = unit(10, 'mm')) + labs(color = "Standardised response")
)

legend = plot_grid(
  geo_legend,
  pred_legend, 
  nrow = 1
)
legend
```

```{r}
plot_grid(
  plot_grid(
    std_mntd_analysis_geo_plot + theme(legend.position="none"), 
    std_mntd_analysis_pred_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''), 
    nrow = 1
  ) + draw_label("MNTD", size = 16, angle = 90, x = 0.01, y = 0.5),
  plot_grid(
    std_gape_fdiv_analysis_geo_plot + theme(legend.position="none"), 
    std_gape_fdiv_analysis_pred_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''), 
    nrow = 1
  ) + draw_label("Beak Width", size = 16, angle = 90, x = 0.01, y = 0.5),
  plot_grid(
    std_hwi_fdiv_analysis_geo_plot + theme(legend.position="none"), 
    std_hwi_fdiv_analysis_pred_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''), 
    nrow = 1
  ) + draw_label("HWI", size = 16, angle = 90, x = 0.01, y = 0.5),
  plot_grid(
    std_mass_fdiv_analysis_geo_plot + theme(legend.position="none"), 
    std_mass_fdiv_analysis_pred_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''), 
    nrow = 1
  ) + draw_label("Mass", size = 16, angle = 90, x = 0.01, y = 0.5), 
  legend,
  nrow = 5
)
ggsave(filename(FIGURES_OUTPUT_DIR, 'process_response.jpg'), width = 3000, height = 3200, units = 'px')
```

```{r}
pred_fig_legend <- ggpubr::get_legend(
  # create some space to the left of the legend
  std_hwi_fdiv_analysis_pred_plot + theme(legend.box.margin = margin(0, 0, 0, -20)) + guides(colour=guide_legend(ncol=2)) + labs(color = "Predictor type")
)
geo_fig_legend <- ggpubr::get_legend(
  # create some space to the left of the legend
  std_mass_fdiv_analysis_geo_plot + theme(legend.box.margin = margin(0, 0, 0, 0), legend.title.position = "top", legend.key.width = unit(10, 'mm')) + labs(color = "Standardised response")
)

remove_x_scale =  scale_x_continuous(name = '', limits = c(-3, 3))
theme_no_legend = theme(legend.position="none", panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"))

fig2 = grid.arrange(
  # row 1 - titles
  arrangeGrob(grid::textGrob('A) Standardised response by city', x = 0.1, hjust = 0, gp=gpar(fontface="bold"))),
  arrangeGrob(grid::textGrob('B) Standardised response predictors', x = 0.1, hjust = 0, gp=gpar(fontface="bold"))),
  # row 2
  arrangeGrob(
    std_mntd_analysis_geo_plot + theme_no_legend, 
    left = "MNTD"
  ),
  arrangeGrob(
    std_mntd_analysis_pred_plot + theme_no_legend + remove_x_scale + ylab('')
  ),
  # row 3
  arrangeGrob(std_gape_fdiv_analysis_geo_plot + theme_no_legend, left = "Beak Width"),
  arrangeGrob(
    std_gape_fdiv_analysis_pred_plot + theme_no_legend + remove_x_scale + ylab('')
  ),
  # row 4
  arrangeGrob(std_hwi_fdiv_analysis_geo_plot + theme_no_legend, left = "HWI"),
  arrangeGrob(
    std_hwi_fdiv_analysis_pred_plot + theme_no_legend + remove_x_scale + ylab('')
  ),
  # row 5
  arrangeGrob(std_mass_fdiv_analysis_geo_plot + theme_no_legend, left = "Mass"),
  arrangeGrob(
    std_mass_fdiv_analysis_pred_plot + theme_no_legend + remove_x_scale + ylab('')
  ),
  # row 6 - legends
  arrangeGrob(geo_fig_legend),
  arrangeGrob(pred_fig_legend),
  heights = c(0.5, 2, 2, 2, 2, 1.25),
  nrow = 6
  )
```

```{r}
jpeg(filename(FIGURES_OUTPUT_DIR, 'figure2.jpg'), width = 183, height = 180, units = 'mm', res = 450)
grid.arrange(
  arrangeGrob(fig2),
  ncol = 1
  )
dev.off()
```

```{r}
pdf(filename(FIGURES_OUTPUT_DIR, 'figure2.pdf'), width = 12, height = 12, family = "Helvetica")
grid.arrange(
  arrangeGrob(fig2),
  ncol = 1
  )
dev.off()
```

# Compare to spatial model

```{r}
plot_grid(
  plot_grid(
    ggdraw() + 
    draw_label(
      "Spatial Model",
      fontface = 'bold',
      x = 0,
      hjust = 0
    ),
    ggdraw() + 
    draw_label(
      "Dredge Result",
      fontface = 'bold',
      x = 0,
      hjust = 0
    )
  ),
  plot_grid(
    std_mntd_analysis_pred_spatial_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''), 
    std_mntd_analysis_pred_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''), 
    nrow = 1
  ) + draw_label("MNTD", size = 16, angle = 90, x = 0.01, y = 0.5),
  plot_grid(
    std_gape_fdiv_analysis_pred_spatial_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''),  
    std_gape_fdiv_analysis_pred_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''), 
    nrow = 1
  ) + draw_label("Beak Width", size = 16, angle = 90, x = 0.01, y = 0.5),
  plot_grid(
    std_hwi_fdiv_analysis_pred_spatial_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''), 
    std_hwi_fdiv_analysis_pred_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''), 
    nrow = 1
  ) + draw_label("HWI", size = 16, angle = 90, x = 0.01, y = 0.5),
  plot_grid(
    std_mass_fdiv_analysis_pred_spatial_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''), 
    std_mass_fdiv_analysis_pred_plot + theme(legend.position="none") + scale_x_continuous(name = '', limits = c(-3, 3)) + ylab(''), 
    nrow = 1
  ) + draw_label("Mass", size = 16, angle = 90, x = 0.01, y = 0.5), 
  nrow = 5
)
ggsave(filename(FIGURES_OUTPUT_DIR, 'process_response_vs_spatial.jpg'), width = 3000, height = 3200, units = 'px')
```



# Compare metrics against each other
```{r}
ggplot(analysis_data, aes(x = beak_width_fdiv_standard, y = mntd_standard, colour = core_realm)) + 
  geom_point() +
  ylab("MNTD") + 
  xlab("Beak Width FDiv") +
  theme_bw() + labs(color = "Realm")
```

```{r}
ggplot(analysis_data, aes(x = hwi_fdiv_standard, y = mntd_standard, colour = core_realm)) + 
  geom_point() +
  ylab("MNTD") + 
  xlab("HWI FDiv") +
  theme_bw() + labs(color = "Realm")
```

```{r}
ggplot(analysis_data, aes(x = hwi_fdiv_standard, y = beak_width_fdiv_standard, colour = core_realm)) + 
  geom_point() +
  ylab("Beak Width FDiv") + 
  xlab("HWI FDiv") +
  theme_bw() + labs(color = "Realm")
```

```{r}
mntd_fdiv_analysis = analysis_data %>% 
  dplyr::select(city_id,  mntd_standard, hwi_fdiv_standard, beak_width_fdiv_standard, mass_fdiv_standard) %>%
  left_join(community_summary) %>%
  mutate(urban_pool_perc = urban_pool_size * 100 / regional_pool_size)
mntd_fdiv_analysis
```

```{r}
ggpairs(mntd_fdiv_analysis %>% dplyr::select(mntd_standard, hwi_fdiv_standard, beak_width_fdiv_standard, mass_fdiv_standard, regional_pool_size, urban_pool_size, urban_pool_perc), columnLabels = c('MNTD', 'HWI FD', 'Bk FD', 'Mss FD', 'Region Rich.', 'Urban Rich.', '% Urban'))
ggsave(filename(FIGURES_OUTPUT_DIR, 'appendix_standarised_correlation.jpg'))
```


